{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "020fc5da-76ac-4334-9628-10c01ddf99df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "metrics = ['node_perf_cpu_migrations_total','node_perf_branch_misses_total','node_perf_context_switches_total','node_perf_cache_bpu_read_misses_total']\n",
    "os.popen(\"kubectl get node -o wide|grep -v 'NAME'|awk '{print $1,$6}'> node.t>node.t\")\n",
    "columns=['timestamp','value','cpu','metrics','node']\n",
    "columns_all=['timestamp','value','metrics']\n",
    "perf_columns=['time','count','unit','events']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5046c8c-6e8b-4ef8-90b4-8cf6b12a47ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evict_port(x):\n",
    "    return x.split(':')[0]\n",
    "\n",
    "def trim_num(x):\n",
    "    return float(\"{:.3f}\".format(float(x)))\n",
    "\n",
    "def reset_time(x,s):\n",
    "    return x-s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ad94bbfd-e207-4781-a590-6a978d5cfa0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perf_total(metric_name='node_perf_cpu_migrations_total',start=1629342630,end=1629343530,step=15):\n",
    "    headers = {\"Authorization\" : \"Bearer eyJrIjoiTlVWSzJjWjEyY2NiSVJuU2hVRXBTMXhMWFZSbEFWNUoiLCJuIjoiZmZmZiIsImlkIjoxfQ==\"}\n",
    "    url = 'http://172.16.101.6:30965/api/datasources/proxy/1/api/v1/query_range?query={metric}&start={start}&end={end}&step={step}'\n",
    "    metric ='sum(rate({mtr}'.format(mtr=metric_name)+ '{}[20s]))'\n",
    "    url=url.format(metric=metric,start=start,end=end,step=step)\n",
    "    print(url)\n",
    "    r = requests.get(url,headers=headers)\n",
    "    df = r.json()['data']['result']\n",
    "    df = pd.json_normalize(df)\n",
    "    dk = pd.DataFrame(columns=columns_all)\n",
    "    for i,r in df.iterrows():\n",
    "        df_values = pd.DataFrame(r['values'], columns=['timestamp','value'])\n",
    "        start_time = df_values['timestamp'][0]\n",
    "        df_values['metrics'] = metric_name\n",
    "        df_values['timestamp']=df_values.apply(lambda x:reset_time(x.timestamp,start_time),axis=1)\n",
    "        dk = dk.append(df_values)\n",
    "    dk['value'] = dk.apply(lambda x:trim_num(x.value),axis=1)\n",
    "    \n",
    "    return dk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e7d5079f-fdb6-4dab-9c8d-d2df43912c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perf(metric_name='node_perf_cpu_migrations_total',start=1629342630,end=1629343530,step=15):\n",
    "    headers = {\"Authorization\" : \"Bearer eyJrIjoiTlVWSzJjWjEyY2NiSVJuU2hVRXBTMXhMWFZSbEFWNUoiLCJuIjoiZmZmZiIsImlkIjoxfQ==\"}\n",
    "    url = 'http://172.16.101.6:30965/api/datasources/proxy/1/api/v1/query_range?query={metric}&start={start}&end={end}&step={step}'\n",
    "    metric ='sum(rate({mtr}'.format(mtr=metric_name)+ '{}[20s])) by (cpu,instance)'\n",
    "    url=url.format(metric=metric,start=start,end=end,step=step)\n",
    "    print(url)\n",
    "    r = requests.get(url,headers=headers)\n",
    "    df = r.json()['data']['result']\n",
    "    df = pd.json_normalize(df)\n",
    "    dk = pd.DataFrame(columns=columns)\n",
    "    for i,r in df.iterrows():\n",
    "        df_values = pd.DataFrame(r['values'], columns=['timestamp','value'])\n",
    "        start_time = df_values['timestamp'][0]\n",
    "        df_values['cpu'] = r['metric.cpu']\n",
    "        df_values['node'] = r['metric.instance']\n",
    "        df_values['metrics'] = metric_name\n",
    "        df_values['timestamp']=df_values.apply(lambda x:reset_time(x.timestamp,start_time),axis=1)\n",
    "        dk = dk.append(df_values)\n",
    "    dk['node'] = dk.apply(lambda x: evict_port(x.node), axis=1)\n",
    "    dk['value'] = dk.apply(lambda x:trim_num(x.value),axis=1)\n",
    "    return dk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c41004d-4adb-45df-af56-1ca2eb7af4fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_ow = 1630138190\n",
    "end_ow = 1630138371\n",
    "interval= end_ow-start_ow\n",
    "platform= 'openwhisk'\n",
    "perf_2_csv_total(start_ow,end_ow,platform,header=True)\n",
    "\n",
    "start_of =  1630138692\n",
    "end_of = 1630138707\n",
    "platform = 'openfaas'\n",
    "plot_end_of=start_of+interval\n",
    "perf_2_csv_total(start_of,plot_end_of,platform)\n",
    "\n",
    "\n",
    "start_kl=1630138802\n",
    "end_kl= 1630138821\n",
    "plot_end_kl= start_kl+interval\n",
    "platform = 'kubeless'\n",
    "perf_2_csv_total(start_kl,plot_end_kl,platform)\n",
    "\n",
    "\n",
    "start_none= 1630138921\n",
    "end_none=start_none+interval\n",
    "platform = 'none'\n",
    "perf_2_csv_total(start_none,end_none,platform)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "ca982558-0756-43f1-8af1-34a5daa6880c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def perf_2_csv_total(start,end,platform,header=False):\n",
    "    start= start-30\n",
    "    end = end + 30\n",
    "    perf_total_df = pd.DataFrame(columns=columns_all)\n",
    "    for m in metrics:\n",
    "        dk = get_perf_total(metric_name=m,start=start,end=end)\n",
    "        perf_total_df = perf_total_df.append(dk)\n",
    "    perf_total_df['platform']=platform\n",
    "    perf_total_df.to_csv('perf_total_df.csv',mode='a',index=False,header=header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "08e5c246-555e-4566-a588-9550cceee79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "      <th>metrics</th>\n",
       "      <th>platform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>512.060</td>\n",
       "      <td>node_perf_cpu_migrations_total</td>\n",
       "      <td>openwhisk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>572.518</td>\n",
       "      <td>node_perf_cpu_migrations_total</td>\n",
       "      <td>openwhisk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>538.800</td>\n",
       "      <td>node_perf_cpu_migrations_total</td>\n",
       "      <td>openwhisk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>1522.865</td>\n",
       "      <td>node_perf_cpu_migrations_total</td>\n",
       "      <td>openwhisk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>2499.639</td>\n",
       "      <td>node_perf_cpu_migrations_total</td>\n",
       "      <td>openwhisk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>180</td>\n",
       "      <td>1318027.987</td>\n",
       "      <td>node_perf_cache_bpu_read_misses_total</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>195</td>\n",
       "      <td>1103600.600</td>\n",
       "      <td>node_perf_cache_bpu_read_misses_total</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>210</td>\n",
       "      <td>1219249.095</td>\n",
       "      <td>node_perf_cache_bpu_read_misses_total</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>225</td>\n",
       "      <td>1161010.200</td>\n",
       "      <td>node_perf_cache_bpu_read_misses_total</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>240</td>\n",
       "      <td>1052786.038</td>\n",
       "      <td>node_perf_cache_bpu_read_misses_total</td>\n",
       "      <td>none</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     timestamp        value                                metrics   platform\n",
       "0            0      512.060         node_perf_cpu_migrations_total  openwhisk\n",
       "1           15      572.518         node_perf_cpu_migrations_total  openwhisk\n",
       "2           30      538.800         node_perf_cpu_migrations_total  openwhisk\n",
       "3           45     1522.865         node_perf_cpu_migrations_total  openwhisk\n",
       "4           60     2499.639         node_perf_cpu_migrations_total  openwhisk\n",
       "..         ...          ...                                    ...        ...\n",
       "267        180  1318027.987  node_perf_cache_bpu_read_misses_total       none\n",
       "268        195  1103600.600  node_perf_cache_bpu_read_misses_total       none\n",
       "269        210  1219249.095  node_perf_cache_bpu_read_misses_total       none\n",
       "270        225  1161010.200  node_perf_cache_bpu_read_misses_total       none\n",
       "271        240  1052786.038  node_perf_cache_bpu_read_misses_total       none\n",
       "\n",
       "[272 rows x 4 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_total_df=pd.read_csv('perf_total_df.csv')\n",
    "perf_total_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "957aeb5c-ca36-42a9-bc37-0c8ece0c4cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp', ylabel='value'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P0WgE5Glc0bhJO+98TbvokHCKlGRLvf1uPbrSokUwKz5ZxcH9hxg4KMbFkYmm5OoJs9u0acPFixevWWf9+vV8/PHHnDljmeC5sLAQXR1GKl+8eJG2bdvaXrdt25by8nKys7Nt77Vu3brCNi1atLD923rRaNmype09T09PCgoKah2DEPaWpzfY/i2jfIQQomm6+uZtrk5PcEjLqlcWwoWkTy9E/Vzdj5c+vWjMpJ13PknuiwY7eiQFlUpFx84daBcZgUqlImHXXknuC7s6d+6c7d9nz56lVauKEwidOXOGuXPn8sknn9hqs40dO9a2XKFQ1HiMVq1a2S4u1uOo1WpatGjB+fPna70fIRoT/dU/BGTkvhBCNElXt+8GSe6LRkz69ELUzx/beSEaK2nnnU/K8ogGS0k+RodO7fH09MTP34+evXqwZ/deV4clmpiVK1dy/vx59Ho9H3zwAbfddluF5UVFRSgUCoKDgwFYs2YNqamptuXBwcFcuHCB0tLSKo8xatQo/vOf/3D69GkKCgp4++23ufXWW5v8zOqiacu93Pn3DwyQR3iFEKKJytXpCAjSAjKiUzRu0qcXon6sCf2AIK0k90WjJu2880lyXzRYSvIxunbvYnsdO3ggvx86Qn5evgujEk3NqFGjeOihh7jpppuIiIhg6tSpFZZ36tSJhx56iEmTJnHddddx7Ngx+vfvb1seGxtLp06duP7664mLi6v0GBMmTGDMmDHcd999jBgxAo1Gw4svvujQ8xLC0awJ/XYd28sPASGEaKL0OXradWwPyIhO0bhJn16I+rHeuG3Xsb3cxBWNmrTzztc8b2kIu8nJ1nHxwsUKyf246wby4b//j6Q9+7hhxDAXRieakl69evHYY49VeC8uLo4dO3bYXj/77LM8++yzlW7v4eHBsmXLKrz31FNPVXitVCqZNm0a06ZNu2b78PBwUlJSKrw3fvx4xo8fb3sdGRl5zTpXxyeEK1iTPBEdozjy2yFMRiNKlcq1QQkhhLAbk9FIniGXdh2iOJy4X57SEo2a9OmFqB9Djh6VWk2bduGcSE6peQMhXETaeeeTkfuiQa6eTNeqd79eeHp6kiCleYQQwuUMOTq8vL1pFdYas8lEXm6eq0MSQghhR3mGXMwmEyFhoXj5eMuITiGEaIIMOj2BQVoCg4MoKiiktKTE1SEJIRoJSe6LBrEm97t062x7z9PTk74D+kjdfSGEaAQMOj0Bl38IwJUa/EIIIZoG68TpgUFatMFBUpZHCNFo/etf/6Jr164cO3bM1aG4nVydzpLct82vondpPEKIxkPK8ogGOXokhbA2rQnUBlZ4P+66gby76D2ys7Jp0bKFi6ITTcUPP/zg6hCEcFsGnY7A4Io/BCI6uDYmIYQQ9mO9aRsQrLVMtCgj90UjJX365u3333/nt99+o02bNq4OxS3pc/RoWwTZBuwYdHpCwkJdHJUQFUk77xoycl80yLHkY3S9qiSPVezggQDs2Z3o7JCEEI2cTqfjkUceYeTIkYwePZpp06aRk5NzzXpLlixh8ODBjB07lrFjx/Lyyy+7IFr3l5tjeYQ34HJyXy9JHyGEaFKsI/W1wUEEBmll5L4QotEpLS1lwYIFzJs3D4VC4epw3NK1I/elTy+EsJDkvqi3oqJi0k6lV5hM16p7z274+fux51cpzSOEqEihUDBlyhS2bdvGpk2biIiIYNGiRZWuO27cODZs2MCGDRuYN2+ekyNtGvSXk/taW1ke+SEghBBNibU0Q0BQENrgICm/JoRodN555x3GjBlDRESEq0NxSyaTiVydgcDgiiP3hRACpCyPaIDjKccxmUwVJtO1UqlUxMQNYM8uSe4LISrSarXExcXZXvft25dVq1a5MKKmq7y8nPzcXAKDg/D29cHDw0N+CAghRBNjyNHhodHg7eNNQLCW/Nw8ysvKUHt4uDo0IYRg//79HDp0iBkzZtR7H4cPH67XdklJSfU+ZmNSVFCI0WgkNz+PE6dOAJB8+HcC24RUWK+pnG9tyfnWnVqtpqCgwA7ROJY7xNgQpaWlFb7Phn63ktwX9WadTLeykfsAcYMH8uP2nzhz+gxtI9o6MzQhhJswmUysWrWK4cOHV7p8y5Yt/PLLL4SEhPDUU0/Rr18/J0fo3vL0BsAyyaJCoSAgWMo1CCFEU2PQ6W3tvNY2otNAi1YtXRyZEELA3r17OXnyJCNGjADg/PnzPPzww/zjH//g+uuvr9U+oqOj8fT0rNNxk5KSGDBgQJ3jbYwyT6UDEN27F7FxcfgF+OPr7VPh/JrS+daGnG/9JCcn4+vra4eIHKegoKDRx9hQGo2GPn36AFe+25KSknrfyJTkvqi3o0dSCAgMIKxN60qXW+vuJ+zey3hJ7gshKvHKK6/g4+PDfffdd82ySZMm8fjjj+Ph4cHOnTt54okn2Lp1K0FBQbXef30vjtA0RoJczDwHQJYuh6SkJNQaDadPpV9zbk3hXOtCzrfpak7nCs3vfEXlDDq9rUxDwOVrpCFHJ8l9IUSj8Oijj/Loo4/aXg8fPpylS5fSpUvlgwTFtayDc6609TJ5uhDiCknui3pLST5G1+5dqpwQp0On9oS0asmeXXsZf9c45wYnRCOxdu1afvzxR959991rln3//fckJiYya9asKrdfsmQJhYWF1a7jruLj40lPT2fp0qUolddOARMScuUx0yFDhhAWFkZqaiqxsbG1PkZ9RvlA0xkJcqDcMql5v5gBdInuzo8Rbcm+cElG+cj5NknN6VzBPqN8RNNgyNEREhYKgDZYa3lPntISwq6kTy9c6Y/J/cAgrW2+FSGEfbhzO++U5L5Op2PmzJlkZGSg0WiIjIxkwYIFKBSKSt8PDg4GLHd0NRqNLTEzY8YMhg4dCsCpU6d4/vnn0ev1aLVa4uPjiYqKcsbpCMBoNJKaksqd90yoch2FQsHAQTH8unMPZrO5ypsAQjRXI0aMsD2e2ty8/fbbHD58mGXLlqHRaCpd58KFC4SGWpIVycnJnDlzhvbt2zszTLdn7fRrr/ohcPJoqgsjEkIIYW8GnZ5Ol+fACrxq5L4Qwjmac5++Pn744QdXh+B2rG16YJDW8t/gIE6lSJ9eCGdp7O28U5L7CoWCKVOm2CZQjI+PZ9GiRcycObPS919//XXbtu+++26lj2vNmzePyZMnM3bsWDZs2MBLL73Ep59+6ozTEUD6qQyKi0uqrLdvFTt4IFs3fsPxYyfo3LWTk6ITrrRp3RbWf7XRIfsed+cYRt9xe43r7dixg8WLF2M0GgkODmbBggWkpaXx1ltv0bNnT44ePYpKpeKNN96gUyfL3+W6detYuXIlRqMRPz8/5s+fT4cOHVi7di2bN28mICCA1NRU/P39WbJkCSEhIdx9993MmTOH3r17M3/+fPbu3cuWLVsoLy9nyJAh/O9//wMgPz+f6dOnX7P91XeGT548yQsvvEBRUREmk4k77riDhx9+uMJ5paSkMGPGDF588cU6jV5vbFJTU1m6dClRUVFMmjQJgPDwcN577z0eeeQRnn76aXr16sXixYv5/fffUSqVeHh4sHDhwgqj+UXNrKN8LpxJY+vn/0eANpw8vQGT0YhSpXJtcMKhjOVGXnv2BboO6N2sRrML0dyYjEbyDLkVSjWAjNwXDdMY+vMgfXohrAw5elRqNb7+foCM3Bf20Rjaemnn7ePaOggOoNVqbQl8gL59+3L27Nkq369JdnY2R44cYdSoUQCMGjWKI0eOkJOTY//gRaWsk+l2uzxKqCq2uvu79jg8JiHA0j7MnDmTRYsWsWnTJkaNGsWMGTMASwN7xx13sG7dOu69915mzpwJQGJiIl9//TUrVqxg7dq1PPzww8yePdu2z0OHDjFr1iy2bNlCp06d+OyzzwAYNGgQv/76K2Apj+Dp6cnFixc5dOgQHTt2xMfHp9rtr7Zy5UqGDRvGxo0b2bx5M3feeWeF5bt372bGjBm8/fbbbv8joHPnzqSkpLBt2zY2bNjAhg0beO+99wBYvnw5vXr1Aiw3fDdv3szGjRtZs2YNN9xwgyvDdksGnQ5PLy/OpZ8k8+RxfPy8MZlM5OXmuTo04WC67GyOHTrC/9ZtpbSkxNXhCCEcJM+Qi9lkso3m1Hhq8PH1xaCTkfvCvUmfXogrrp44HSwj94uLiigpLnZxZELUn7Tz9uP0mvsmk4lVq1YxfPjwWr0/Y8YMzGYzAwYM4LnnniMgIIBz584RGhqK6vKoQ5VKRatWrTh37pytpI9wrKNHUtBoNER1iKp2vTZtw4hoF87eXxO578HJzgnOjk6nZzLr2dm8/d6bhF6uZSqqN/qO22s9GscRDhw4QLdu3Wx3dSdMmMDLL79MQUEBkZGRtsZ17NixvPjii+Tn5/PDDz9w9OhRJk6cCIDZbCY3N9e2z/79+xMWFgZAnz592LVrF2C5QHzwwQeMHj0arVZLbGwsu3fvJjMzk8GDB9e4/dUGDhxIfHw8ZWVlxMXFMWjQINuyX375hZ9//pmPPvrIVqZGiNow5OgJDNaSZ9AD4OnlAUDu5R8IounSZ1kGPOQbcvl23WZGTaq6jJ4Qwn3p/1CqASAgWEZ0ioZxdX8epE8vxNVydboK7XzgVfOrtApr7ZqghNtzdVsv7bz9OD25/8orr+Dj48N9991X4/srVqwgLCyM0tJSXnvtNRYsWMCiRYvsFkt9Jx9LSkqyWwzuoLLz3ZuQSFh4aw4ePFDj9h06t2fPrr3s2bPHdkOmMbv6fLdt2c6RQ8n85//+y4hbbnRhVI5hj79ltVpNQUGBHaKxj+LiYkwmky0mk8lkW1bZ+wUFBZSWljJmzBimTp1aYV8FBQWUlJSgUqls25WXl1NSUkJBQQHdunXj999/59tvvyUmJoa+ffuyYcMGzp49y+OPP17j9iUlJZSXl1NQUMD1119P165d2b17N0uXLuWLL77gtddeo7S0lIiICE6cOEFSUlK1o9dLS0srfKcywaIw6HQEBgfZkvsqlWW0jyFHT0QHFwYmHE6XbUnuB7YIZuNnX3Lj7SNtj3IL4SxPPPEEmZmZKJVKfHx8ePHFF+nevXuFdYxGI6+++io///wzCoWCRx991PaDTdQs93L5nYDLiR6wzLMiNfeFu6vPnG1ms5kJEybwzDPPVLrcOpcfWAYIGo1GAAYMGMCRI0f48ccfGTx4MLGxsaxZs4bMzEyefvrpGre/2siRI+nbty87d+5k+fLlrFmzxpbDaN++PampqRw+fFiS+6JO9Dl6tC2CbK+tiX5Djk6S+8JtSTtvP05N7sfHx5Oens7SpUtRKpU1vm+9W6LRaJg8ebIt8RYWFsaFCxcwGo22D/vixYu29WsrOjq6whdXG0lJSc2qdm1l52s2mzl35jx/GjGsVp9F1oUcfvr+Z7w8vOndr5ejQrWLP57vqo+/BODcmQtN7nu3199ycnIyvr6+dojIPgYNGsSCBQs4f/48HTt2ZM2aNfTo0QNfX19Onz5NcnIyMTExbNiwgS5duhAaGsrIkSOZNWsW9913H61bt8ZoNJKcnGxrI9Rqte0cr37t6+tLz549+c9//sPixYvp2bMnr776KjqdjkGDBtkmBK9q+6v/nZ6eTrt27YiKiqJLly7Mnj0bX19f22TjL730kq2O22233VbpuWs0Gvr06QNYvt/o6GhJ8DdzuTl6Wke0tSX3zWZL50TKNTR9uqxsAG66czRrl33KxhVfcs/jD7k4KtHcxMfH4+/vD8D27duZPXs269atq7DOpk2byMjI4Ntvv0Wv1zNu3DgGDx5MeHi4K0J2O9ba+taJ08FSd//0iTTXBCSEnfTr1485c+Zw4sQJOnbsyLp162x9+vT0dBITE4mJiWHTpk106dIFPz8/hg8fzqxZs7j77ruv6dNXR6PR0KNHD5YvX27r08+ZMwedTmfrW9dWeno6ERERjB8/nsjIyArlItq2bcvs2bN5+OGHKSkpqbJPL8Qf5ep0RHZqb3ttnWdFntIS7kzaeftxWnL/7bff5vDhwyxbtgyNRlPj+4WFhRiNRvz9/TGbzWzdutU20qdFixZ0796dzZs3M3bsWDZv3kz37t2lJI+TXLxwCV2OrsbJdK0GxsUAkLB7b6NP7l/NbDbz2z7Lkwl7f0203UwSjVtwcDALFy5kxowZlJeXExwczJtvvklaWpqt3Xj99ddRKpUsXLgQsDxWNX36dKZOnYrRaKSsrIxbbrmlxgsEwODBgzl06BDR0dGo1WratWtHeHh4hfasNr7++ms2bdqEh4cHCoWiwgUCoHXr1nzyySc8/PDDFBUVMWGClNgQNTPo9HTp1YMLp9MBMJaXWt6XEZ1Nni4rG5VaTUSnKIbcfCPfrtnEzeNH06KVTEotnMea2AfLBGWVjc7aunUrEydORKlUEhwczE033cQ333zDlClTnBmq27KW5QkIqjii87DcxBVuTvr0QliYTCZydQZbQh8qjtwXwl1JO29HZic4duyYuUuXLuabb77ZPGbMGPOYMWPMTzzxRJXvm81mc0ZGhnns2LHmUaNGmW+77TbzU089Zb5w4YJtn8ePHzffeeed5ptvvtl85513mk+cOFHreIqLi82JiYnm4uLiOp9LYmJinbdxZ5Wd708//Gzu0ynGvC/xt1rvZ+Koe8xT7nvcnqE5xNXnm5mRae7TKcb80D2PmPt0ijEf/O2wCyOzP3v9LR85csQu+3G0H3/80XzHHXe4OgyHuvq7sLZx9W3r3F1Dz70ptPVlZWXme2+43fz5so/Nrzz5oPmVJx80b/jPcvMDN40zr/z3R7b1msK51kVzOd9/v/6W+em7HjAnJiaaL52/YH7gpnHmpf9429VhOVRz+W6trOfb2Nv62bNnm2+44QbzsGHDzMeOHbtm+ahRo8wHDhywvV62bJn5lVdeqdW+pU9vNq9470PzA3++w2wymWzvrf/0c/O9N9xuLikusb3XVM63NprTuZrN0qdviqRPf4W082Zzrk5vvveG281fr15ve8/az1/z8Qrbe03lfGtLzrd+3KGtb47tvNncsPbOKSP3O3fuTEpKSqXLqno/IiKC9evXV7nPjh07snr1anuEJ+ooJfkYAF26dqr1NnHXDeSLz76iuLgYLy8vR4VmV7/tOwjAI08+TNID09izew+9+vR0cVRCCFE7eXoDAN7eV8rP5Rn0lokWL5dxEE2XPiuH4JYtAGgZ2oo/3zGKr1ev57a7xhHRIcq1wYlm5bXXXgNg/fr1LFy4kOXLl9v9GM15Hq2TJ07g7efDvn37bO/pci3t/86ffybgqgkYm8L51lZzOldomvNoVefqebSaIplHS1zN2m+/ekJdtVqNX0CAjNwXQgAumFBXuL+UIylEREbg61f7Ouuxg2P57/+t5LekAwwaEufA6OznwP6D+Pr6MnBQDF26dWbP7r08/PiDrg5L1FNMTAxr1651dRhCOI31h4CHxlJOzNc/gDyDjsAgrdTnbAZ02Tm0aXelZvmY++7ixy3f8uXy//C3f8xzYWSiuRo3bhwvvfQSOp2OoKtKyISFhXH27Fl69+4NwLlz52jTpk2d9t2c59H6btV6QkJDK5yLssTI9tUbaRceQcduljKaTeV8a6M5nSs03Xm0qhITE1PtIMCmQObRElezJfevKstjeS0DdkTTJLmbulPWvIoQFaUkH6Nbj6512mbAwH6o1Sr27N7roKjs77ekA/TqG41KpSJ28ED2Jx6guLjY1WEJIUStWBP4istX+jaR7cnT6y8n92WUT1Onu5RN0OWR+wB+Af6Mvnci+3fv5egBSRAIxysoKODcuXO21z/88AOBgYFotdoK691yyy2sXr0ak8lETk4O27dvZ+TIkU6O1n0ZcnQEBmsrvBdw+bW09UII4f4kuS+EqIkk90Wd5OXlczojs9aT6Vr5+PoQ3SeaBDdJ7ufn5XP82An6DrCMmIi7LpbS0lIOXC7VI4QQjZ21s282lQMQ1i6KkuIi/AID5IdAE1dcVExhQQHaFsEV3h85YTRBLVuwaunHmM1mF0UnmouioiKeeeYZRo8ezdixY/nkk09YunQpCoWCRx55hEOHDgEwduxYwsPDufnmm7nrrrt48skniYiIcHH07sOg01co1QCgvZwAkqe0hBDC/Vlv1P6xrQ8MCpKbuEIIQMryiDpKPZoKQLcedUvuA8QOiuHDf39Mbm4eAQH+9g7Nrg4d+B2TyUSffpZHxK1PHiTs3kvcdbEujk4IIWpm7eyXl5Xg7etHUMtWAPj5+ZCnN2AymVAq5R5/U6TPzgEgqGXF5L7G05M7H7qX5QvfZe+OXcTeMMQV4YlmomXLlnz55ZeVLru67r5KpeLll192VlhNisloJM+Qe81ozoDLT0dI0kcIIdyfIUePSq3G19+vwvuBwVJqUwhhIb/qRZ1YJ9Pt2r1uZXkAYgcPxGQykbRnX80ru9iB/QdRKpW2CXRtTx7s2uPiyIQQonYMOh2eXl4UFeTjH6jF/3Kyx9PLA5PJRH5unmsDFA5jS+63aHHNsutvHkHbqHZ8+eGnlJeXOzs0IYQd5epzMZtM14zm9NB44OPnK09pCSFEE2B9QkuhUFR4PzAoiJLiYooLi1wUmRCisZDkvqiTo0dSCG4RTMuQaxMGNendtxdeXp7s2dX4S/Mc2HeQTl064nfV3fG462JJPnyUXEmIiau88MIL3H777UyfPt3VoQhRQa5OT2CwlnyDHr9ALf6BlpGdKrXlh4GM6Gy6dFnZAARVcq1WqVXc9chfOX/6DD9t/c7ZoQkh7Migq7xUA1hK80g7L0TtSZ9eNFa5Ol2l7bz1qS25kStE7TTldl7K8og6sUym2+Wau8a1ofHU0C+mHwm7G/fod6PRyMH9h7ht7C0V3o8bPJAPliwnMSGJ4X/+k2uCE41KVlYW27ZtIzExUcqbiEbHkGMZ5ZOn1xES1hZ/reUHgEJhsi2P6ODKCIWj6Gwj94M5n3XxmuX9r4ulS68erP1kJUP+fCNe3l7ODlEIYQe5VUyyCBAQJBMtClFb0qcXjZk+R4+2xbXtvDXhb9DpCW0b5uSohHAvTb2dl+S+qLWy0jKOp57gL9dPrvc+YgcP5J03l3DpYhYhrVraMTr7OZF6koKCAvr2713h/V59ovHy9iJh1x5J7jdyO3bsYPHixRiNRoKDg1mwYAFpaWm89dZb9OzZk6NHj6JSqXjjjTfo1KkTAOvWrWPlypUYjUb8/PyYP38+HTp0YO3atWzevJmAgABSU1Px9/dnyZIleHt7c//991NcXMwdd9zBHXfcweDBg3n55ZcpKiqipKSEu+66iwceeACATZs28emnn1JWVgbArFmzGDx4MCaTiQULFvDrr7+i0Wjw8fHh888/d9VHJ5oQQ46OVm3DyM9Nw1+rRePpiae3N0ajpRSLdcSnaHp0Wdl4enni7etT6XKFQsGkxx5kwbS/883q9Yy7f5KTIxRC2IPeOslisPaaZdrgINKOn3RyRELYl/TphbCM3I/qfO2IHGvbL09pCXcm7bx9SHJf1NrJE6coLyuvV719q7jrBgKwZ/debh97q71Cs6vfkg4A0Ld/nwrve2g8GBDb3y3KCrnSz9u+d1iphxtu+zNDR46odp3s7GxmzpzJZ599RqdOnVi9ejUzZsxg2rRppKSkMHfuXGJjY1m3bh0zZ85k7dq1JCYm8vXXX7NixQo0Gg0//fQTs2fPtjXUhw4dYuPGjYSFhTF37lw+++wznn32WZYtW8aECRPYsGEDAPn5+XzyySdoNBoKCgqYOHEiQ4cOpWPHjlx//fWMGjUKhULByZMneeCBB9ixYwdHjx5l9+7dfP311yiVSgwGg0M+O9H8GHR6OvXoQqHBbCvJ4x8YRFlJkW25aJp0WTloWwRX+5Rdl+juxAwdzObP1zB8zK0EaAOdGKEQwh6s7XhAUCUj94O1kvAR9ebq/jxIn14IAJPJRK7OQICU5REO4Oq2Xtp5+5Hkvqg162S63Xp0qfc+unbvQkBgQKNO7h/Yf5CWIS1oE97mmmVxgwey+Kd3uHD+IqGtW7kgOlGTAwcO0K1bN9td3QkTJvDyyy9TUFBAZGQksbGxAIwdO5YXX3yR/Px8fvjhB44ePcrEiRMBMJvN5Obm2vbZv39/wsIsjzr26dOHXbt2VXrs4uJi5s+fT0pKCgqFgosXL3L06FE6duzI6dOn+dvf/saFCxdQq9VkZWVx6dIlIiIiMBqNzJkzh7i4OG688UZHfjyimTCWG8nPzcPLxxMAv0AtAP7aIArz81B7qCXp04Tps3MIalnz3Dh3TbmfpJ0JrP/v59z/1GNOiEwIYU+5OXo8NBq8fbyvWaYNDqKooJDSkhI0np4uiE6IhpE+vRBQkJuH0WistOZ+QGAgCoVC+vTCbUk7bz+S3Be1dvRICl7eXkRERtR7HyqVioFxA9izey9ms7letfsd7cC+g/Tt36fS2OKuszQue3bvZfQdtzs7NLcwdOSIWo3GcZT6/F2ZzWYmTJjAM888U+lyz6t+FKtUKoxGY6XrLV68mJCQEN544w3UajUPPfQQJSUlADz33HM8//zz3HTTTZhMJvr06UNJSQkhISFs2bKFhIQEdu/ezaJFi1i3bh0hISF1OgchrpZrMGA2m/H08gDA35rcD9SSdf4sgUFBMsqnCdNlZdO+a+ca12sTGcGfbvsz32/4mlsmjKVVm9ZOiE4IYS8GnR5tcFCl/R5bLeYcPSFhoU6OTLg7V/fnQfr0QsCVUfmVJfdVahX+gQGS3Bf15uq2Xtp5+2l6swgIh0lJPkaXrp1RqVQN2s/AwTGcO3uezIwzdorMfgx6A5mnz9DnD/X2rTp37URQkJaE3VKap7Hq168fycnJnDhxArDUY+vRowe+vr6kp6eTmJgIWOqodenSBT8/P4YPH86GDRs4f/48YJlU+fDhw3U+dl5eHq1bt0atVnPs2DHbsazLwsPDAfjqq68oLS0FICcnh+LiYoYNG8aMGTPw9/fn9OnTDfoMhLB28lUqS2fp6pH7+bkGAoMDMeToXRSdcCSz2Yw+O4fgWozcBxj/wGRUKhWrP/qvgyMTQtibIUdXaakGuFKqR27kCnclfXohrkruVzJxOsjk6cK9STtvPzJyX9SK2WwmJfkYt44e2eB9xQ22jH5P2L2HiMjwBu/Pno4fs0w81qdf5cl9pVLJwMExJOza02ifPGjugoODWbhwITNmzKC8vJzg4GDefPNN0tLS6N69O5s3b+b1119HqVSycOFCAAYOHMj06dOZOnUqRqORsrIybrnlFqKjo+t07KlTpzJz5kw2btxIu3btGDhwoG3ZCy+8wBNPPEFoaCixsbFotVoAzp07x4svvkh5eTlGo5Fhw4bRt29fe30copm6krg3oVAo8PMPACBAG4TZZCIg0B9dtr6qzYUbKyoopKS4BG3L4FqtH9SyBbdMHMvGz77ktrvvoH2XTg6OUAhhLwZd1aPytdZazDKiU7gp6dMLUXNyPzBI5lcR7kvaefuR5L6olbOZZ8nPy6dr9/rX27eK6hBJSGgIe3bv5c5J4+0Qnf0cTzmBRqOhe89uVa4Td10s327dTtrJdNp3jHJecKLWhg0bxrBhwyq8l5aWhlKpZP78+ZVuM2bMGMaMGXPN++PHj2f8+PGVvg4PDychIcG2rEePHmzevLnS/Y8bN45x48bZXj/77LO2faxdu7ZW5yVEbVl/CBjLS/H1D0B5+Ykra3keHz9fTqWmuSY44VC6rGwAglrULrkPMGrSBH7Y+A1ffPAJz7/1qqNCE0LYmUGnp1MVfVbriH4Z0SncmfTpRXNnTdxXVpYHLEn/1N+TnRiREPYl7bx9SFkeUStXJtPt2uB9KRQK4gYPZO/uREwmU4P3Z0/Hj52gZ+8eeGg8qlwnbrDljl7C7j3OCksIIeokV2f5IVBaXGRL6IOlLA+Al7cHeXpDo2uDRcPpsnIAajWhrpWPny/j/nI3h5N+41DifkeFJoSwI2O5kTy9oeqET1AgAAadjOgUQgh3ZcjRo1Kr8fX3q3R5YLAWQ44es9ns5MiEEI2JJPdFrRw9koJSqaRTl4522V/s4IHodHpSU47bZX/2UFJSQvrJDPpWUW/fKrxdOG3C25CwS+ruu5OYmJhGe5dVCHvT5+jw9PKkMD8Pv8Arj/Fak/seGjUmk4n83DxXhSgcRJdtGbmvrcPIfYARY28jpHUon3/widz0EcIN5BlyMZvNVSb31R4e+AX4y/wqosmRPr1oTgw6PYFB2irLAQcGB1FaUkJxUZGTIxPCcaSdrzunJPd1Oh2PPPIII0eOZPTo0UybNo2cHMvIslOnTnH33XczcuRI7r77btLS0mzb1XeZsL+U5GNEdYjEy8vLLvuzjn7f04gmpj1yKBmj0VjlZLpXi7tuIIkJSVXOvC2EEK6Uq9MTGBxEnkFfYeS+r58/SqUKhcIyukdqdDY9V0bu1y2576Hx4M6H7yM99QS//rDDEaEJIezIOiK/qjrMcHmiRWnnhRDCbRlydFXexIUr5XrkRq4QzZtTkvsKhYIpU6awbds2Nm3aREREBIsWLQJg3rx5TJ48mW3btjF58mReeukl23b1XSbsLyX5mF1K8liFhoUS2b5doxr9/tu+gwD07luL5P7gWPJy80j+/aijwxJCiDoz5OgJDAq0jNy/PAEQgEKpxC8wEJOpzLaeaFr02Tn4+PniWY+b8YNH3EBkpw6s/vC/lJWWOSA6IYS95FonWawm6aMNDpJ2Xggh3JhBpycgWFvl8ivJfbmRK0Rz5pTkvlarJS4uzva6b9++nD17luzsbI4cOcKoUaMAGDVqFEeOHCEnJ6fey4T96XV6zp+7YJfJdK8WO3gg+xL3U1ZWbtf91teB/QcJDQsluEXVI6CsYgfHAJCwS+ruCyEaH4NOj1+gpTbn1SP3La+DKCspubye/BBoanRZ2XWqt381pVLJpMce4NL5C3y/caudIxNC2JPeOsliNUmfgCCttPNCCOHGcnU6tNU8oWV9eksmTxeieVM7+4Amk4lVq1YxfPhwzp07R2hoKCqVCgCVSkWrVq04d+4cZrO5XsuCg2v/GPrhw4frdQ5JSUn12s5dbdqwBQClWmHXc28Z2oLCgkLWrF5D566d7Lbf+jCbzSTt2UfvftG1Psfwdm3Zvu0H+g6seaR/Y2WP71OtVlNQUGCHaBzPXeKsr9LS0grfaX3bOOH+DDk6ojpFUJRbSXJfq+XimUzLejo9XsEBLohQOIouK5ugOtbbv1p0TD969u/D+k+/YNgtN+Hj52vH6IQQ9mJN5AQEVZ30kZH7QgjhvkwmE7k6AwHVluW5nNzP0RHsF+akyIQQjY3Tk/uvvPIKPj4+3HfffRw5csTZh68gOjoaT0/POm2TlJTEgAEDHBRR45OUlITZaKnNPGrMKIKqGR1UVx07dGTpP5eTm5Pn8s80PS2DvNw8OnXtWOtY/jTiBr5c8RU9e/a021wEzmSvv+Xk5GR8fRt/8qegoMAt4mwIjUZDnz59AMv3Gx0dLQn+ZshYbiQ/Nw9Pb8v17drkfhAnj/6O2kNNrk5PKO1cEKVwFF12Dt379qr39gqFgkmPPciLj01ny+drmTjlL3aMTghhL7k5ejSennj7eFe5TkCQluKiIkqKi50YmRBCCHsoyM3DaDRWW34tQBuAQqnEoNMT3E6S+0I0V04py2MVHx9Peno6//znP1EqlYSFhXHhwgXbpKRGo5GLFy8SFhZW72XC/o4eSSG0dSu7JvYBtEFaunbvQkIjmFT3t6QDAHTq0qHW28QOHkhpaSkHLtfqF41D165d6zRCPzMzs0LZsNpKSEhg/Pjxdd5OVD/J+tWMRiMvv/wyN910E3/+859ZvXq1C6J1P7kGA2azGQ8Py9NtfpWU5SktLkYbHGQr6yCaBpPJhD5b16CR+wDtu3Zi0PBhfL16PbpsKXkoRGOkvzzJokKhqHIdrZRrEG5M+vSiubO23dWV5VGqVPgHBkjNfeG2pK23D6cl999++20OHz7Me++9h0ajAaBFixZ0796dzZs3A7B582a6d+9OcHBwvZcJ+7P3ZLpXix08kIP7D1FU5NoRRQf2H8Q/wJ/WbVrXepsBA/uhVqsaxc0JIdxJdZOsX23Tpk1kZGTw7bff8sUXX7BkyRIyMzNdELF7yb1cgkGhMKNUqfDx9auw3P/yBLsBWn8p19DE5OfmYSwvr3fN/atNfPgvGI1G1n6y0g6RCSHsLVenr7ZUA1ypxy9JHyGEcD9Xyq9pq11PG6yVm7hCNHNOKcuTmprK0qVLiYqKYtKkSQCEh4fz3nvvMX/+fJ5//nnef/99AgICiI+Pt21X32XCfkpLS0k7mc6Im290yP7jrhvIpx99xv7E37hu6CCHHKM2Duw7SJ9+vVEqa3+/y9fPl+g+0ezZtRf+5sDg3MzBhJ38tvsXh+y77+Dr6R03pFbrmkwm3njjDbKyspg2bRr33HMPCQkJgOVu74QJE2yvwfJk0d69eykpKWHevHnExFgmTf7pp5/497//TWlpKR4eHrzwwgv07dv3muNVtd7Jkyd54YUXKCoqwmQycccdd/Dwww+zfft23nnnHZRKJUajkRdffLFed6DdUWWTrK9ateqa9bZu3crEiRNRKpUEBwdz00038c033zBlyhRnhut2rKPxTcYy/AK0KP7QrvkHWkb/+AX4yA+BJkaXlQ1gl+R+aNswRoy5le3rt3DrnWNpExnR4H0KIezHoNPTKqz6QSnWhJAhR4/CR+OEqERT0Vj68yB9etF8WfvpgdWM3AfL3CsyYEfUR2Nt6x9++GEeeughaevrwCnJ/c6dO5OSklLpso4dO1ZZaqG+y4T9nDl9FqPRSNfuXRyy//4x/VB7qNmze6/Lkvu5hlxOpJ7kllEj67xt7OCBfPj+/5Gbm0dAgL8DohP1UVJSwgsvvEDbtm156623OHPmTLXr6/V6unbtyqxZs9izZw/PPfcc27dv5/z587z//vt89NFH+Pn5kZqayiOPPMKPP/5YYfuMjIwq11u5ciXDhg3jySefBMBgMADw7rvv2i5CRqORoqIih3wWjd3Vk6z/0blz52jTpo3tdVhYGOfPn3dmeG7J+kOgrLSYgMuj9K8WoLX8QPDy9iLj5FknRiYczZbcb2BZHquxf7mbHV9v58sPP2X6K3Pssk8hhH0YdHo69exW7Tq2sjw5OrQ+oc4ISwi7kj69aM6sT11VV3Pfuvz86er/3xCiMftjW3/8+PFq15e2/lpOn1BXuJeMtNMADivL4+3jTe8+vdjjwtI2B387BEDfAX3qvO2g62JZ9q8PSUxIYvif/2TnyNxT77ghdbpD6whTpkzh9ttv5+GHH67V+h4eHowZMwaA2NhYvLy8OHnyJElJSWRkZHDvvffa1i0vLycrK6vC9j///HOV6w0cOJD4+HjKysqIi4tj0CDLTaxBgwbxxhtvcMsttzBs2DC6dHHMDbTG7upJ1h2hIRMKJyUl2TES5zlyyHLOel0O/kEtrjkPY3kZACWlhRh0eswmk9uea3011fM9lLQfgNPnzmAovlK7siHn22/YIHZv+x9b1m2kdbu2DY7R0Zrqd1uVxny+Op2OmTNnkpGRgUajITIykgULFlxTRnPJkiWsXLmSVq1aAdC/f3/mzZvnipDdhrHcSJ7eUGPCx18bCFhuBGjDJbkvaq8x9OdB+vSieTPk6FGp1fj6+1W7XmBwkKVPbzY7KTLRVEhb33Taeknui2plpJ3Gz8+XNuFtal65nmKvG8gHS5Zj0BsIvPwjxJkO7DuISqUiulcPjiQfqdO2vfpE4+XtxZ7deyW534jExcXx888/c8899+Dj44NarcZkMtmWl5SUVLu92Wy2TVA3dOhQFi5ceM06J06cqPC6qvVGjhxJ37592blzJ8uXL2fNmjUsWrSI2bNnk5KSwq+//sozzzzDgw8+yF133VWf03Vb1knWly5dWmlJrLCwMM6ePUvv3r2Ba0fy10Z0dDSenp51ji0pKYkBAwbUebvG4GjCfjy9PDGWldAuKqrS89jx1WcEBPpjNpkoKixi6A3DXBCpa7jzd1uTjMPHABh6wzDUHh5Aw8+3S8dO7N72PxRlxkb/uTXl77Yy1vMtKSlp0I1MR7HOr2J9ZDk+Pp5Fixbx+uuvX7PuuHHjmDVrlrNDdFt5hlzMZnONyX21Wi0TLQq3Jn160ZwZdPoaJ04Hy8j9stJSSour//9BiMbqj229SqWqcLNK2vqaOW1CXeGeTqdl0rV7lxovKA0RN3ggZrOZxATXjD77bd9BunbvgrePd5239dB4MGBgPxJ27nFAZKK+pk2bxnXXXceUKVPIz8+nZcuWlJeXk56eDmCbjNuqrKyMTZs2AZCYmEhJSQnt27dnyJAh/Pzzz6SmptrWPXjw4DXHq2699PR0QkJCGD9+PE8++SSHDlmeFDl58iRdu3blr3/9K2PGjLG931xUNsn6H91yyy2sXr0ak8lETk4O27dvZ+TIupfPam4MOXoCggIpKSrCL1Bb6Tr+2iDMJiMAhXn5ToxOOJIuK5sAbaAtsW8PfoEBePv6cOncBbvtUzQPlc2vcvaslAKzB4PucqmGGuowgyXpI/OrCHclfXrRnBlydDXexIUr14LCfOnTC/f0x7a+RYsWlJWVSVtfBzJyX1TJaDSSmXGGuOtiHXqc6N498fbxZs+viYwYeW3dbUcqLy/n0IHD3DFxbL33EXddLIvfeIcL5y8S2rqVHaMTDfHoo4/i5eXFAw88wIcffsiMGTN48MEHadu27TUTn2i1WtLT05k4cSLFxcUsXrwYjUZDVFQUb775JnPmzKG4uJiysjL69+9vG0luVd16X3/9NZs2bcLDwwOFQsHs2bMBeOutt0hPT0elUhEQEMBrr73mtM/G1aqbZP2RRx7h6aefplevXowdO5YDBw5w8803A/Dkk08SESGTetbE8kMgAMjFv5rkvu7SJQAK8woqXUe4H112jl0m072aQqEgpHUrDDodp06dori42K77tye1Wk1ycrKrw3AYLy8vwsPD8bDjzRtnqW5+FYAtW7bwyy+/EBISwlNPPUW/fv3qtP/6PrnQmMsaVSftqKUW7fmLF2o8B4WHmjOnMwH3Pd/6aE7nCvY5X7VaTUFB4+oTFBYWcu+996JUKrn//vv517/+xYwZM3jggQcICwuzTaBYUFBAUVERWq2W1NRUJkyYQHFxMa+99hplZWWEhITwyiuv8Pzzz1NSUkJZWRl9+/alY8eOFBcXYzKZKCgoqHa9DRs28PXXX9v69H/7298oKChg4cKFZGRkoFKp8Pf356WXXmrw51haWlrhO22MT2cJxzPo9Ghb1OImbrAWgALp0ws3dnX+5t1332XOnDmSv6kLczNUXFxsTkxMNBcXF9d528TERAdE1DidOnHK3KdTjHn9VxsdfqwnHnrKPO7mOx1+nD/6/dARc59OMeZvNm8zm831+36Tfz9q7tMpxrxp3RZ7h+dQ9vpbPnLkiF3242j5+fmuDsHhrv4urG1cfds6d9fQc3fntn7WA0+Y35w12/zKkw+aTx79vdJ1Nn72kXnRzKfN995wu/m/H/yfkyN0LXf+bmsy95FnzAtnzavwnj3Od/HsV8xb1m4wX7p0yWwymRq8P0dpyu28yWQyX7p0yXzy5Enbe9bv1h3a+vnz55unTp1qNhqN1yy7ePGiubS01Gw2m82//PKLedCgQeacnJxa7be59ul3fLPdfO8Nt5vPnc6scd33Xllofvaeh936fOuqOZ2r2Sx9+qZI+vRXNNd23mw2m6dN+It5Wfw/a1wv/fhJ87033G5e+dF/nBBV4+Hu329dNae2vjm282Zzw9o7KcsjqpSSbKnd66jJdK8WO3ggp06mceH8RYcf62oH9lkevenTv3cNa1atS7fOBAVp+XWXlOYRQrherk6Pl7dlnoEqR+4HBlFcaHl0Vx7hbTpysrIJahFc84p11LJ1K/y1gQQHBzu0TJ+omkKhoEWLFo36yYmqWOdX+ec//1np/CohISG2pxGGDBlCWFhYhcekxbWsZXZqU5YnIEiLIUfv2ICEEKIGTzzxBGPGjGHcuHFMnjy5ST9pZw8mk4lcnYGAupTlkVKbQjRbktwXVTp6JAWVSkWHju0dfqy4wZbSP3t/TXT4sa52YN9BWoeF0jqsdb33oVQqGTg4hj279soM9UIIlzKWG8kz5KLxtFTdq7bmvtmMp7eH/BBoIozlRnJ1eruX5QEIaR0KgPmqSQyF87njjZXazK9y4cKV+RySk5M5c+YM7ds7vu/pzgw5OjSennh51zxflDY4iJLiYkprmIxOCCEcKT4+no0bN7J+/XoeeughW6kLUbmC3DyMRmOtau77B/ijUCopkD69EM2WJPdFlVKSj9Emog0eGsfXdu3SvTPaoEASnDz6/bd9Bxo0at8qdvBALl64SPqpdDtEJYQQ9ZNrMGA2m1EqFXhoNHh6VZ74Cbic9NcGaaXmfhNh0Okwm81oHTByPyTMktwvKyu3+75rkpCQwLBhw+q9fXFxMY8//jgDBgzg6aeftmNkoibW+VUuXrzIpEmTGDt2LE8++SQAjzzyiG0issWLFzNq1CjGjBnD3LlzWbhwISEhIa4MvdEz5OgJDNLW6oZPQJB1RKe09UII1/H397f9Oz8/3y1vWDuT9QktbS2e0FKqVARoA2XAjhDNmEyoK6qUknyMbtHdnHIspVJJTFwMe3ZbRr8742J//tx5zp+7wF/792nwvqxPHvy6aw9RHaIavD8hhKiPXGvpBbMRv8CqEz/+WssPBb8AXxnl00TosnMACGrpmLI8GadPU15eBnjZff/2snHjRjZu3MiqVats733zzTdkZWWRkJCAWi3dXmfq3LkzKSkplS5bvny57d/x8fHOCqnJyNXpa1WqAUBrm2hR2nohhGvNmTOHnTt3Yjab+fDDD10dTqNmTe7Xtq0PlAE7QjRr8itHVOrSxSyys3JoFxnutGPGDR7I9m++JyMtg8j2kQ4/nj3q7VuFt2tLm/A27Nm1l0n33dXg/QkhRH0YdDoAjOWlVdbbhyvJfW8fTy6cNzgjNOFg+ixrct8xZXkyTp+m3AUj9xvq7NmzREVF1SuxX15eLjcERKNk0OlpVcuSkoFBUotZCNE4vPbaawCsX7+ehQsXVrjRW5PDhw/X65hJSUn12s7Vjl7OVWSeO0uRqazG9RUqS1kedz3f+pLzrTu1Wk1BQeO/EeQOMTZEaWlphe+zod+t/GIRlUpJtoy0ioiKcNoxY68bCEDCrr1OSe7/tu8gXt5edO7aucH7UigUlpsT237AaDSiUqnsEKEQQtSNdZRPSVEhrdq0rXI9H18/lCoVGi8PCvObdsepubCN3G9h/+S+j58vCqWC8rKaf1zW1/Dhw7n77rvZsGEDly5d4qabbmL+/PnXrLds2TK+/PJLsrOzCQsL49lnn+XPf/4zJ06c4PXXX6e8vJx+/fqhUqm4//77WbZsGWazme+//57Zs2czYcIEli5dyurVqykuLmbo0KG8+OKL+Pv7k5mZyYgRI3j11Vd57733aNu2LRMmTODLL7+kd+/erF27lsDAQN58803S0tJ45513KC0tZebMmdxxxx0O+2yE+CNDjo5OPWv3dG3A5ZH7Mnm6EKKxGDduHC+99BI6nY6goJrLzgBER0fj6elZp+MkJSUxYMCA+oTochdPngbguuuH4BfgX8PasLf9TxzY477nWx/u/P3Wh73ONzk5GV9fXztE5DgFBQWNPsaG0mg09OljqSJi/W5LSkrqfSNTau6LSqUkHwMgwokj99tFRtA6LJQ9u/c65XgH9h+kV59oPDzsc48rdvBA8nLzSP79qF32J4QQdWXIsYzcLyzIq3bkvkKpxD9Qi0qloDC/AJNMlOr2dJeyUSqVBGgDHLJ/lUrl0OQ+wKZNm/joo4/47rvvOHXqFO+///4160RERLBixQqSkpKYNm0af//737l48SIdO3Zk9uzZ9O3bl/3795OYmMjTTz/NY489xq233sr+/fuZOHEia9euZd26dXz66ads376dwsJCFixYUOEYe/fuZevWrXz00UcAHDx4kK5du5KQkMCoUaN47rnnOHToEN999x1vvvkmCxYsaPKji0TjYZ04vTaTLAIEBAaiUCgoyJXkvhDCNQoKCjh37pzt9Q8//EBgYCBardZ1QTVyhhw9KrUaX3+/Wq0fGKSlMDcfs9ns4MiEEI2RJPdFpY4eOUZ4RFt8fCqfjNERFAoFAwfFsDchyeGJpqLCIlKOHKNPv4aX5LGKHRwD4LSbE0II8UcGnR5vH0/Ky8rwqya5D5dL85iNmE0m8nPznBOgcBhddjaBwUEoHfTkmFKlcnhZnnvvvZewsDC0Wi1Tp05ly5Yt16xz6623EhoailKp5LbbbiMyMpKDBw/W+hibNm3igQceICIiAl9fX5577jm2bt1KefmVc3vqqafw8fHBy8syv0B4eDgTJkxApVJx2223ce7cOZ588kk0Gg3XX389Go2GjIyMhn8AQtRCniEXs9lMYC0mWQRQqVX4BwbIU1pCCJcpKirimWeeYfTo0YwdO5ZPPvmEpUuXyqS61TDoaj9xOkBgcBBGo1HaeiGaKSnLIyqVknyMbj26Ov24cdfFsmndFo4lp9Ktp+OOf/jQEYxGI30H2C+536JlCzp37UTCrr089NgDdtuvqJuuXbvy7LPP8t1336HX65k5cyYjR44EYMeOHSxevBij0UhwcDALFiwgMjKShIQEXn/9dfr06cP+/ftRKBS8/fbbdOzYEYB169axcuVKjEYjfn5+zJ8/nw4dOrjyNIWolCFHT2BwIEC1I/ety3MuXry8nY4AbaCjwxMOpMvKcchkulYqlZLysjKHTnofFhZm+3ebNm24ePnv82rr16/n448/5syZMwAUFhaiuzzXRG1cvHiRtm2vlKxq27Yt5eXlZGdn295r3bpiLfMWV5U6sib8W7ZsaXvP09NTRu4Lp7E+oVXbkftgSfoUysh94WakT990tGzZki+//NLVYbgVQ46ubu385XUNOn2tR/sL0RhU1tZff/31gLT1dSHJfXGNgvwCMtIyGD3uNqcfO+66WFQqFRvWbHJoct86mW7vvr3sut+462JZvXINJSUlda4J2FScPPU7J0/Wr05YTTp0iKZD+541rufn58eaNWtISkpi+vTpjBw5kpycHGbOnMlnn31Gp06dWL16NTNmzGD16tUAHD9+nH/84x8sWLCAf//737z//vu89dZbJCYm8vXXX7NixQo0Gg0//fQTs2fP5vPPP3fIOQrRELk6Pf4BfkA+fjU86uyvDaKkuAiw/BBw3gwrwhH02Tm1nmCzPpQqFSazGaPR6LBJZq9+ZP/s2bO0atWqwvIzZ84wd+5cPvnkE1td/bFjx9qW1+amQ6tWrWw3BqzHUavVtGjRgvPnz9d6P0K4inXi9LomfbKzshwUkWiKGkN/HqRPL5ovg05PUIvaD9qwTp5uyNHRpp3zSisL99ZY2/pvvvmG7OxsaevrwCnJ/fj4eLZt28aZM2fYtGkTXbp0ITMzkyeffNK2Tl5eHvn5+ezZswewTKym0WhsCdIZM2YwdOhQAE6dOsXzzz+PXq9Hq9USHx9PVFSUM06lWTiWchyArt27OP3YIa1aMvbO0az+fA1/efhe2rQNq3mjevgt6QAdOrUnINC+tYljBw/ks49XcmDfQWIHD7TrvkXt3Xab5cZU3759uXjxIiUlJRw6dIhu3brRqVMnACZMmMDLL79M/uUJ5tq3b0+PHj1s2/3vf/8DLDUhjx49ysSJEwEwm83k5uY6+5SEqBV9jo7gln6UFuXjH1h9yQb/wCCM5WUolQpyL0/EK9yXLiuHrr1r14GuD+tE8eVl5Q5L7q9cuZIbb7wRLy8vPvjgA1tbblVUVIRCoSA42PJjd82aNaSmptqWBwcHc+HCBUpLS9FoNJUeY9SoUSxfvpxhw4YRHBzM22+/za233uqwcxLC3qwTpwdenii3NgKDg8g4leaQeIRwJOnTi+YqV6cjqnPtRxpbrwkG6dMLN1RZW3/gwAFp6+vAKb9kRowYwf3338+9995rey88PJwNGzbYXr/22msYjcYK27377rt06XJtgnnevHlMnjyZsWPHsmHDBl566SU+/fRTx51AM5OSnAJA1x5dyTxz2unHf+zJKWxet5Wl7y5jQfw8u+/fZDJx8LdD3HTLcLvve8DAfqjVKn7dtcchyf3MjEx+/nEnk/5yV6MdWdihfc9a36F1FOtNQVsy6nIt5eo+s6sTQUql0raN2WxmwoQJPPPMM44KVwi7ydXpCQsPorQI/AOrL7Pjf3lkv9pDiT6n9mVNRONTVlpGfm5unUZ41ZXSltwvA28vhxxj1KhRPPTQQ1y8eJERI0YwderUCvX0O3XqxEMPPcSkSZNQKBSMGzeO/v3725bHxsbSqVMnrr/+ehQKBQkJCdccY8KECVy4cIH77ruPkpISrr/+el588UWHnI8QjnAluV+7mvtweaLFvAKHltUSTUtj6M+D9OlF82QymcjVGQioY/k1uFK6TYjaaKxtvdForLHPIm19RU6ZUDcmJqZCHdU/Ki0tZdOmTUyYMKHGfWVnZ3PkyBFGjRoFWH4IHjlyhJycHLvF29ylHDlGUJCWVqEhLjl+aFgod993J5vXb+Xk8VN23/+pE2nkGnLtOpmula+fL9F9otmzy/6T6uZk63j8gWnEv7KIE8dP2n3/TV3v3r1JTk7mxIkTgKUOW48ePfDzq74m4fDhw9mwYYOtXIPRaOTwYcc8uiZEQxjLjeQZclGrlXh5++Chqb40mL/W8iPA09NDRu67Od3levFaR9bcV1q6jOVlZQ47Rq9evdi6dSuJiYnEx8fj7e1NXFwcO3bssK3z7LPPsmfPHhISEnjhhRf47LPPbCNzPDw8WLZsmW05WCbHXbRokW17pVLJtGnT+Omnn/j1119ZtGgRgZdvhIWHh5OSklJhFP/48eNZtWqV7XVkZCQpKSkV4t6xYwcxMTH2/0CEqIQhR4fG0xMvb+9abxMQrKW8rIzioiIHRiaEc0ifXjR1Bbl5GI3GOpVf8wvwR6FUysh90WT069dP2vo6aBTPIP/www+EhobSs2fFO0YzZszAbDYzYMAAnnvuOQICAjh37hyhoaG2OzoqlYpWrVpx7tw522PaomFSko/RpXsXl47seeixB1nzxXre/+dSFv0r3q77PrDfMgqw74A+dt2vVezggXz4/v+Rm5tHQIC/XfZZXFzM9Mf/xoXzlskFk/bso1PnjnbZd3MRFBTEwoULmTFjBuXl5QQHB/Pmm2/WuN3AgQOZPn06U6dOxWg0UlZWxi233EJ0dLQTohai9vIMuZjNZsCEXw2T6QK2sj0+ft4YcvQOjU04lj7LMsDBkSP3FUolKqWK8rJyhx1DCFEzQ46ewCBtnfrp2qtGdHr7+DgqNCGcQvr0oqmzJui1dXhCS6lU4uPnK3160WQEBwdLW18HjSK5v2bNmmtG7a9YsYKwsDBKS0t57bXXWLBgQYWRV/ZQ37s3SUlJdo2jMSkvN3IsJZWbbrnRdp6uOt+bbrmRjWu2sObLtUR1jLTbfr//7n/4+ftxKesiWdmXrlne0PPVtgjEZDLxxcov6T+wb4P2BZbH8j5490MOHTjM49Mf4YtPV/PdN9vp1K3hs33b47tVq9UUFBQ0eD/2sm/fPgBbTNbXAAMGDOCzzz6rsH5BQQHR0dH897//tW3zx9cjRoxgxIgR12zX2JSWllb4TpvDHWpxhfUxXJOxDG1wzU9e+V++AeDl4ymjfNycLvtycr9lC4ceR63xoMyBI/eFEDXL1enrVG8frky+a8jR0zq8rf2DEsIB/viUlPV1QUEBw4YNY9iwYddsExcXx9q1a6t8PWbMGMaMGeOgiIWwD2u/vC5leQB8/f2kLI9wO5W19dZci7T1tefy5P6FCxfYu3cvCxcurPC+tYyPRqNh8uTJTJ061fb+hQsXMBqNqFQqjEYjFy9erLbsT1Wio6NttZ1qKykpiQEDBtT5WO4iNeU45WXlDPvTMAYMGODS8+3apSs7fviF7V//j39/vMRu+12Q/joxsf0rfYTeHufbu1dv3l34HtkXc+zy2b3z5hISf93Hs7Oe5q9T/sLpU5ns3LGb/v37N+jpCnt9t8nJyfj6+jZ4P45WUFDgFnE2hEajoU8fyxMpSUlJREdHS4K/GTHoLJ35stLiWo3c99Bo8PbxxWgqkx8Cbk6XdbksjwNH7oPlZm5pSalD9v3DDz84ZL9CNDX6HB2hber2u0dqMQshhPuwttV1mVsFwMffVwbsCNFMOaXmfnXWrVvHDTfcQFDQlYarsLCQvLw8wDIZwtatW+nevTsALVq0oHv37mzevBmAzZs30717dynJYycpyccA6Nr92omMnc3P34+HH3+Q3b/8yt4E+zw9oMvRk34qgz79HVOSB8BD48GAgf3sUnd/zRfr+HjZp0y8ZwL3P3wfADGxA9Dl6BwyH4EQwn1ZO/NFhQW2evo18dcGofKQ+pzuTp+dg4eHB352KgVXFbWHB+XlZZfLPwkhXCFXpyegviP3pa0XQohGrz5leQB8/GTkvhDNlVOS+6+++irDhg3j/PnzPPjgg9x+++22ZevWrbumJE92djZ/+ctfGD16NKNGjeLUqVPMmzfPtnz+/Pl89tlnjBw5ks8++4yXX37ZGafRLBw9koKXlydRHexXBqchJk6eQKvQVix56z27JBMOWuvt97f/ZLpXix08kJMnTtlq5NfHrp938/q8eIbccB2zXpphG6UfE9cfgL0JiXaJVQjRNBh0elQqBWaTyVZypyb+Wi1KhZlcnR6TyeTYAIXD5FzKRtsy2OFz5Xh4qDGbzRjLjQ49jhCictaJ0+ua8PEPDEChUKCXpI8QQjR6hhw9ag81Pn51e+rcx98Pg04vgzCEaIacUpZn7ty5zJ07t9Jl27Ztu+a9iIgI1q9fX+X+OnbsyOrVq+0VnrhKSvIxOnXpZJuw2NW8vLx47KkpvDL3dXb88DM3jLi23lZd/LbvAGoPNT16dbdThJWLuy4WgL2/JjJq3G113j415Th/f+oFOnbuwMJ/vo5afeV/1bYRbWkdFkpiwj4m3XeX3WJuCLPZ7NIJmAWSmBWXJ0r0Aqh9cj8wCMxGTCYTBbl5+GsDHRihcBR9dg5BLRxbbx8sI/cBysvLUHu4vLKjEM2OdeL0utZhVqpUePv5kisj90UNpE/vetKnFwadngBt3SZOB/D198VYXk5BXr7Dn+YU7k3aetdyxA04l5flEY2H2WwmJfkY3Xp0dXUoFYwZP5qIyAj+9fa/G9zZObDvED16dq/zXAt11aVbZ4KCtCTs2lPnbS9euMRTj0zHx8+HJcvexvcPd+wVCgUxcQNISkhqFHflvby8yM7ObhSxNEdms5nS0lLOnDnT5OcUENUz5OgJ0Fo68rWpuQ+WsjwmYzmAjOh0Y7qsHLQtHV+e0JrQLy8rd/ixhBDXstVhrmNyH8DHT2oxi+pJn961pE8vrAw5uvq18/6W3wHS1ovqSFvvWmazmezsbLy8vOy6Xxl2JWzOnT1PriG3UdTbv5qHh5onpz/O88/O4ZvN33LbmFvqtZ+y0jJ+P3SEu+6dUPPKDaRUKokZFEPCrr11uitaWFDIM489h8GQy8crlxMaFlrpejGxA9i8fisnjp+kU+eO9gy9zsLDw8nMzOTSpUsujaMmpaWlaDQaV4fhEGq1msDAQFq2bOnqUIQL5er0+Pr7AIV1GLlvWU99ue5+hMOiE46ky86md2x/hx/HNnK/rMzhxxJCXMs6cXp9kj6+/lKLWVRP+vSuJ316AZbkfFCLug/a8PG33BQy5OhoGym9elE5d2jrm3I7D5YbLOHh4XbdpyT3hU1jmkz3j26+7Sb+74NP+Pc7H/DnW2/Cox7lAI4eSaGkpIS+DpxM92px1w3ku6+3k34qnagOUTWubzQaeeG5uaQkH+OdDxbTrWfVT1BY6+4nJiS5PLnv4eFB+/btXRpDbSQlJdGnj3O+eyFcwaDTow3ypKykEL+A2pXXsU68q1YrpVyDmyoqLKS4sIggJ4zcVyqVqFQqyiS5X8HatWv58ccfeffdd69Z9v3335OYmMisWbOq3H7JkiUUFhZWu44QcGU0ZmAda+6DpRbzpcxzdo5INCXSpxeiccjV6Yjq3KHO2/n4+wHIjVxRLXdo66WdrzspyyNsUpKPoVQq6dyts6tDuYZSqWTac09wOiOTDV9trNc+frs8mW7vfo6dTNcqbrCl7n7C7r21Wn/Ra2/z0w8/M+ulGQz905Bq120b0ZawNq1JTNjX4DiFEE2DQafHQ6PC1z8Albp2N0ADLif3PTxU8givm9JnW37ABbV0fM19sIzel7I8tTdixAhJ2gu7sZXlCdbWeVufyyP35TF8IYRovEwmk6XUZj2f0AIpyyNEcyTJfWFz9EgKkVHt8Pa2b+0nexn6pyH0HdCHD977kOLi4jpvfyDpAG3D2xDSyjmPOYa3a0tY2zASdtZcd3/lfz5n1X+/4L4HJ3P3vRNrXF+hUDCgEdXdF0K4lrHcSJ7egEJhrnW9fbgycl/jqZZRPm5Kl5UNOC+57+GhbhRleXbs2MG4ceMYPXo0f/3rX0lPTycxMZExY8bwwgsvcMcdd3DnnXdy/Phx2zbr1q1j4sSJjB8/nvvvv5+TJ08ClpH3Dz30ENOnT+f2229n0qRJtkeV7777bg4etAwOmD9/PrfffjsA5eXlxMXFUVhYCEB+fn6l269du5ann34agJMnT3L33XczZswYRo0axUcffXTNeaWkpDB69Gj27Kn7nD2i6TPo9Gg8PfHy9q7ztr7+vpSVlVFUUOiAyIQQQthDQW4eJpMJbT2e0PLy9kKlUkmfXohmSMryCN5/dREDh11HSvIx+vTt5epwqqRQKHjquSd4+N7H+Py/q3ngkb/Ueluz2cxv+w4Qd12sAyOsSKFQMOi6WLZv+wGj0YhKpap0vR+//4k3X1vM8D//iWdnPV3r/cfE9mfzui2Nou6+EMK18gy5mM1mzGZjrevtA3j7+qFUqfDx88GQo3dYfMJxrMl9bT1qs9aH2sODzeu/ZufOBIfsf9ydYxh9x+3VrpOdnc3MmTP57LPP6NSpE6tXr2bGjBlMmzaNlJQU5s6dS2xsLOvWrWPmzJmsXbuWxMREvv76a1asWIFGo+Gnn35i9uzZfP755wAcOnSIjRs3EhYWxty5c/nss8949tlnGTRoEL/++iu9e/cmKSkJT09PLl68yJkzZ+jYsSM+Pj7Vbn+1lStXMmzYMJ588kkADAZDheW7d+/m9ddf5+2336ZTp072+khdRqfTMXPmTDIyMtBoNERGRrJgwQKCgyv+rRqNRl599VV+/vlnFAoFjz76KBMn1jzQoTky5OgJDNbWei6nq1nLNehzdPj4yWSdQgjRGNnKr9Vj5L5CqSQgSCsj94Vohuo0cr+srIzExES2bt0KQGFhoW3EknBPeXoDu7b/yI5vtnPuzDm69qi6zntjMCC2P0OGDebjZf8hLy+/1tudzTxL1qVs+jipJI9V7OCB5OXmcfRISqXLjxxO5vln59KjV3dee+uVKm8AVGZg3ADAUndfCFeQa0LjYZ1ksbyspE4j9xUKBZ7evnh5a+SHgJvSZecAOKXmPliS+2bMLn1q7MCBA3Tr1s2WAJ8wYQLJyckUFBQQGRlJbKzlRv7YsWM5duwY+fn5/PDDDxw9epSJEycyduxY3nrrLc6fP2/bZ//+/QkLCwOgT58+ZGRkADBo0CB27drFuXPn0Gq13HjjjezevZtdu3YxePDgGre/2sCBA1mzZg3//Oc/2b17NwEBAbZlv/zyC6+99hoffvhho0rsN6SdVygUTJkyhW3btrFp0yYiIiJYtGjRNett2rSJjIwMvv32W7744guWLFlCZmamXc+jqTDk6OqV8IEryX2ZX0UIUV/S93c8a3+8PmV5wFK2Tfr0QjQ/tR65n5KSwtSpU9FoNFy4cIHbbruNvXv3sm7dOv75z386METhSJlplh+faakngMY5me4fTXvuCe4Z9xf++9FnPDH98Vpt89s+yyP1fQc4d1KO2MExACTs2kPPXj0qLDt75hxPP/osQcFBvLP0rTqXQ2oT3sZWd3/SfXfZLWYhakOuCY2LNVlTWlxUp5H7AF6+vhQXlsgPATelz8rBy9sb78sjyB1N7aHmxhHDmPzAZLx96l4axB7MZnOdRy6bzWYmTJjAM888U+lyT09P279VKhVGoxGAAQMGcOTIEX788UcGDx5MbGwsa9asITMz01Zup7rtrzZy5Ej69u3Lzp07Wb58OWvWrLElu9u3b09qaiqHDx8mNDS0TufmKA1t57VaLXFxcbbXffv2ZdWqVdest3XrViZOnIhSqSQ4OJibbrqJb775hilTptjzdJoEg05PaJuwem0rtZiFEA0hfX/nuDK3St3L8oBlxL+U5RGi+al1cn/+/Pk8/fTTjBs3joEDBwKWEUhz5851WHDC8azJfV1WDgqgW4/Gn9zv3rMbf771Jv778Uom/eUugmtRiuDA/oP4+vrSsR6zzjdEi5Yt6Ny1Ewm79vLQYw/Y3s/Ly+fpR5+luLiEpf95j5YhdZ8HwFp3f+ePO+uV6BCiIeSa0Ljoc3So1ZaH8eqc3PfxRanMlh8CbkqXneO0evsAarUHwOW6+65J7vfr1485c+Zw4sQJOnbsyLp16+jRowe+vr622vsxMTFs2rSJLl264Ofnx/Dhw5k1axZ33303rVu3xmg0kpycTHR0dLXH0mg09OjRg+XLl7N48WJ69uzJnDlz0Ol09OlTtwED6enpREREMH78eCIjI5k9e7ZtWdu2bZk9ezYPP/wwJSUl3HbbbfX6bOzJnu28yWRi1apVDB8+/Jpl586do02bNrbXYWFhFZ6qqI3Dhw/XOSaApCT3evox+1IWQaEt6xW3deT+od8OoPZ3zf+7zuRu321Dyfk2bfVt4+xJ+v7OYb0BW5+a+2C5KZBxIs1+AQkh3EKtk/vHjx9n7NixALYkoo+PDyUlJY6JTDhF5ql0yz/MZlqHhtQqUd4YPPHMY3y/7Qc+WvoJf5/zXI3r/5Z0gF59o+tU9sZeYgcP5KtVaykpKcHT05OysnL+/tTzpJ1M472P3m1QvXxb3f3UE3Tq0nge4xdNX0OuCfHx8Wzbto0zZ87Ykm9/tGTJElauXEmrVq0AS8mLefPm2fEMmhaDTo/ao37JfU8fX0zGcnJ1ekwmE0plnSr2CRfTZWU7rSQPWEbuAy6dVDc4OJiFCxcyY8YMysvLCQ4O5s033yQtLY3u3buzefNmXn/9dZRKJQsXLgQsCYjp06czdepUjEYjZWVl3HLLLTUm9wEGDx7MoUOHiI6ORq1W065dO8LDw9FoNHWK++uvv2bTpk14eHigUCgqJPcBWrduzSeffMLDDz9MUVEREyZMqNP+7c2eff9XXnkFHx8f7rvvPrvGaBUdHV3h6YnaSEpKYsCAAQ6JxxGM5UaKCwrp1LVLveJO3LsXpVJJoF+AW513fbjbd9tQcr5NW1JSEtHR0S5P8Es+yDkMOXrUHup6z40SGKSVPr0QzVCtk/tt27bl8OHD9Op1ZcLVgwcP0q5dO4cEJpwj81Q6AZcvABFtWrs6nFpr3zGKMeNH8eWKr7jvwcmEVRN7fl4+x4+d4NFpNzoxwiviBg9kxSerOLDvIAMHxfD6/Df4dWcC8//xYoMn+L1Sd3+fJPeFUzXkmjBixAjuv/9+7r333mrXGzduHLNmzWpwrM1Bbo4eL29LYstPW7eRPl4+fpjNJsBMQW4e/tpAB0QoHEWXlU3nnt2ddjylUolapaa8rNxpx6zMsGHDGDZsWIX30tLSUCqVzJ8/v9JtxowZw5gxY655f/z48YwfP77K14899hiPPfaY7fXy5ctrvf3V/3788cd5/PFrywk+9dRTtn+HhISwcePGSuN3Nnv1/ePj40lPT2fp0qWVJhrCwsI4e/YsvXtb5kX640h+YZFrMGA2m+tdh9k60aJentISQtSD5IOcw6DTE6Ct38TpYBm5bzQaKcjLxz8woOYNhBBNQq1v5T3zzDM89thjvPvuu5SVlfHBBx/wzDPPMH36dAeGJxzJbDZzJi2D3rH9MZnNBNbz7rCrPPbUIwAs+9eH1a53+ODvmEwmp0+mazUgtj8qlYqE3Xv5eNl/WPflBqZMfYhxd16bYKirK3X3m9ejqcL1GnJNiImJsU08KezDoNPjF2Bpw+tTlgdA7aGUWsxuxmw2o8/KcerIfbCM3i8vd93IfeEc9uj7v/322xw+fJj33nuvyicdbrnlFlavXo3JZCInJ4ft27czcuRIO51F05Gboweo94S61m1zdZLcF0LUneSDnKMhE6fDlWuETJ4uRPNS65H7N954I8uXL2f16tUMHDiQM2fOsGTJklo9ziwaJ32OjvzcPLz9/SkpKwfTtZO/NWZhbVozcfIEPv/vl/x1yn1EdYiqdL3f9h1EqVTSq09P5wZ4ma+fL9F9erL283XodHpuuf1mnpj+WM0b1oJCoSAmbgC//LhTHr0TTuWMa8KWLVv45ZdfCAkJ4amnnqJfv3513kdDHmF2p3qup9PSUXsoUSjMHE05hqIObYGnryW57+GhYu+ve7iQk+WoMBsNd/puq1NcWEhZWRn5RYXVnpM9zletVlNQUGB5oVRQWlJ65XUjERMTw3//+99GF5c9lZaWVvg+Hfm33NB2PjU1laVLlxIVFcWkSZMACA8P57333uORRx7h6aefplevXowdO5YDBw5w8803A/Dkk08SERHhsPNyV4bLSfn61mEGCAjSyk1cIUS9SD7IOQw6PUENKJVsnYhXn6OjbZQ8VSFEc1Hr5D5Az5496dnTNQlSYX9nTlkm0y0uK6O4rJx8N+zsT5n6IOtWb+D9dz5g4Tv/qHSdA/sO0qlLR/wuTyTmCnGDB3Jg30H6DujDy/Ev2TUJHxM3gE3rtnDy+EkpzSOcypHXhEmTJvH444/j4eHBzp07eeKJJ9i6dStBQXVLatSnDjO4Xz3Xr4wf4xvog4+vBzGXJzmrrZ9/zAVArVYSGhLiVuddH+723Vbn9Mk0AHr17VPlOdnrfJOTk/G9fCOopLCIksIifHx8GtVk7gUFBbYYmyqNRmObyNf63ZaUlDisFnND2vnOnTuTkpJS6bKrSxupVCpefvnleh2jOdFfHrlf37I8YLkxcDb9tH0CEkI0O5IPcrxcnY6ozh3qvb01uS83csUflZeV8fu+g/SK6YvSBXNRCseqdXL/nXfeqXLZM888Y5dghHNZJ9O9mJWDSakkz5BLnt7gVvWWg1sEc98D97D8/f/jwUf/Svee3SosNxqNHNx/iNvG3uKiCC1G3zGK8+cu8OysZ+qVaKxOjNTdFy7g6GtCSEiI7d9DhgwhLCyM1NRUYmMbNk9FU2UZ5dOiziV5wDKhLkhZHnekz84BILhlC6ceV+3hgRkoLy/Hw8PDqccWziN9/8bFWk4nMFhb730EBmsx6HSYzeZGdWNOCNH4yTXB8UwmE4YcfYNu4lrL8hhkfhVxlRNHj7E8/h0yT6Uzbd4sBt041NUhCTurdXL//PnzFV5funSJvXv3ctNNN9W4bXx8PNu2bePMmTNs2rSJLl26ADB8+HA0Go0t2TljxgyGDrX8kZ06dYrnn38evV6PVqslPj6eqKioGpeJ2jt9Kh3/wABOnEijVdswjDk5nD6VTg8X1aavr/un/IUvVnzFvxa/z3sfvVth2YnUkxQUFNC3fx8XRWcRERnOgvh5Dtl32/A2hLUNIzEhiUl/ucshxxDijxpyTaiNCxcuEBoaClhGDJ85c4b27dvbZd9NjcloJM+Qi8IcVK/kvkqlwsfPD42uGMPlkaHCPeiyLMl9rdNr7lsS+uVlZZLcb8Ic3c6LujHo9Gg8PfHy9q73PgKDgygvK6cwvwBfFz7RKoRwP3JNcLyC3DxMJlODyq/5+vuhUqulTy8AKCkuZs3HK/l69Xq0wUGoVCrSj5+U5H4TVOvk/j/+cW3Jkx07drBly5Yatx0xYgT3338/99577zXL3n33XVuy/2rz5s1j8uTJjB07lg0bNvDSSy/x6aef1rhM1N6ZtHTC20ey5cdd3Hr7zaTs3E3GiVNul9z39/fjwUf/yjtvLiFpzz4GxPa3LTuw7yAAffu71znVVUxsf6m7L5yqIdeEV199lW+//ZasrCwefPBBtFotW7ZsqVCDefHixfz+++8olUo8PDxYuHBhhdH84opcfS5mkwljeRl+9UjuA/gHBqHLzpNRPm5Gl5UNgDbYucl9Dw9L97G8rNypxxXO1ZB2XtifIUdPYLC2QSPurx7RKcl9IURdyDXB8axP0DZkQl2FQkGgzK8igOTfDvHhm+9y4cw5bhx9C/c89iALpv2dzMtlPUXT0qAs4PXXX8/27dtrXC8mJoawsLBa7zc7O5sjR44watQoAEaNGsWRI0fIycmpdpmoPbPZTOapDAJbtqCwoJDoPtH4BwZw+mS6q0Orl0l/uYuQVi1Zsvh9zGaz7f3f9h0gpFVL2oS3cWF0jhcTNwCdTs/J4yddHYpoxmp7TZg7dy47duzgyJEj7Ny50/ajYPny5fTq1QuwPPG1efNmNm7cyJo1a7jhhhscGrs7M+h0KBRQXl5ar5H7AP7aIDw8VPJDwM3osnPwC/BH46lx6nFVamtyv8ypx3WVF154gdtvv53p06e7OhSXq207L+zPkKNrUMIHKk60KIQQDSXXBPuy9sMbUpYHLG29DNhpvgoLCvl48Xu8Nv0FzGYzs99+nYf/Ng0fP1/C20eSeXnuTdG01Hrk/unTFSdfKioqYvPmzXVK2ldmxowZmM1mBgwYwHPPPUdAQADnzp0jNDQU1eVJHlQqFa1ateLcuXOYzeYqlwU7eeSaO8u+cInioiK4PMq7W8+uJHeIIvNUmmsDqydvby8efXIKr817g19+2sXQPw0BLCP3+/Tr3eTrirqq7v7u73/iRPIx7pv2iNOOKRoHR10TRN3l6vSo1Za2vP4j97UoVTL5lrvRZWWjbeH8vo9SqUStVjeLkftZWVls27aNxMTEZvdknLTzjYtBpye0TcM+e+vNgVxp64UQdSTXBMezJuQDG1CWByxtvS5bBr82RwcSEvm/t94jJyubW+8ax50P3Yenl5dteUSHKH79388UFRbi7ePjwkiFvdU6uf/nP/8ZhUJhGxXt7e1N9+7deeONN+p98BUrVhAWFkZpaSmvvfYaCxYsYNGiRfXeX10dPny4XtslJSXZORLnO5V8zPLf05moVEoMuXo0ft6k/n6UxL17UVz1A9Zdzrddx3BCWrVk4auL8PLVkJebR+bpM1z3p8F1Ogd3Od8/atEymO++2U7nHh1rvU1DzrW8rIyP//lvivIL6NQ/Gg8njxytD3f9buurvm1cbTjimiDqx6DTo/aw3PD2D6zfjwF/bRCYTbYJG4V70GfnEOTkevtWag8Pylw4cn/Hjh0sXrwYo9FIcHAwCxYsIC0tjbfeeouePXty9OhRVCoVb7zxBp06WW56r1u3jpUrV2I0GvHz82P+/Pl06NCBtWvXsnnzZgICAkhNTcXf358lS5bg7e3N/fffT3FxMXfccQd33HEHgwcP5uWXX6aoqIiSkhLuuusuHnjgAQA2bdrEp59+avtcZs2axeDBgzGZTCxYsIBff/0VjUaDj48Pn3/+uas+ulqTdr5xydXp6RLdvUH7sJXlkeS+EKKO5JrgeNa2uSE198EyeXpa6gk7RCTcRZ4hlxXvLeeXb/9H26h2zPvXm3Tq0fWa9cLbRwJw5lQGnXp2c3aYwoFqndw/evSo3Q9uvcur0WiYPHkyU6dOtb1/4cIFjEYjKpUKo9HIxYsXCQsLw2w2V7msrqKjo22T+dZWUlISAwYMqPOxGptzx04BUFxSTodOHRg0eBBF2QZ++zmBiDbhhLa1fJ7udr7TZz7NnBkvkX1BZ6sJPHrM7fTqG12r7d3tfK923dDB/PLjTvr161er0YUNPdfvN35NYV4+AMH+gXTp1aPe+3IGd/5u6yMpKYno6GiHJfgdcU0Q9aPP0dlG7vtrtfXah7/W8iOiIC9X5u5wI7qsHNpGtXP6cX/e9j3b12/BZDJVGA1kDzfc9meGjhxR7TrZ2dnMnDmTzz77jE6dOrF69WpmzJjBtGnTSElJYe7cucTGxrJu3TpmzpzJ2rVrSUxM5Ouvv2bFihVoNBp++uknZs+ebUuyHzp0iI0bNxIWFsbcuXP57LPPePbZZ1m2bBkTJkxgw4YNAOTn5/PJJ5+g0WgoKChg4sSJDB06lI4dO3L99dczatQoFAoFJ0+e5IEHHmDHjh0cPXqU3bt38/XXX6NUKjEYDHb9zBxF2vnGw1humTi9oaM5fQP8UalUUpZHCFFnck1wPEOOHrWHGh8/3wbtJzA4iFydXvr0zYDZbGbPTzv5zztLKcjNY9z9kxh73914aDwqXT+iQxQAp0+lS3K/iXHZ/+mFhYXk5eUBlj/IrVu30r27ZTRKixYt6N69O5s3bwZg8+bNdO/eneDg4GqXidrLTMsgqGUwqcdO0LW7ZUJj6//oGSdPuTCyhrll1M106tKR9/65lMQ9+/D09KRbJXcsmyJr3f0TqY6vu28sN7Ll8zW0jmgLwMmUVIcfUwhRudwcPZ5elidn6l1z//J2KiUU5ObZKTLhSCaj0TJyv0ULlxxfoVBWmOPGmQ4cOEC3bt1sI/InTJhAcnIyBQUFREZGEhsbC8DYsWM5duwY+fn5/PDDDxw9epSJEycyduxY3nrrLc6fP2/bZ//+/W0DRfr06UNGRuX1SIuLi5k9ezajR4/mnnvu4eLFi7aEx+nTp3n44Ye5/fbbefbZZ8nKyuLSpUtERERgNBqZM2cO69evd+AnI5qqXIMBs9nc4DrMSqWSgKBAKcsjhBCNkCFHR4C2YROng+UpLZPJRL706Zs0XXYO77z0Okvmv0GLkJa8suyf3PnQfVUm9gFatm6Fp5cXp920HLeoWrUj92+44YZaNSw//vhjtctfffVVvv32W7KysnjwwQfRarUsXbqUp556CqPRiMlkomPHjsybN8+2zfz583n++ed5//33CQgIID4+vlbLRO1knkontG1bfj6QbEvuh0e1Q6FQkHkynYFDr3NxhPWjUqmY9txUpj8+g69WraVXn+hqG7em5Erd/SQ6d3Vs3f29O3Zy8ex5nnllNp++s5RTKccdejzRONjrmiDsy6DT4+vnjUqtxsunfiN9rCP31Zcn1fXXBtozROEAuXrLUxZaF5TlGTpyBH0HxXDp/EUi2kc5/TprNpvr/MPXbDYzYcIEnnnmmUqXX/0kp/XJ0MosXryYkJAQ3njjDdRqNQ899BAlJSUAPPfcczz//PPcdNNNmEwm+vTpQ0lJCSEhIWzZsoWEhAR2797NokWLWLduHSEhIXU6B2eQdr5xys3RAzR4Ql2wjOiUkftCiNqQa4JzGXT6Bj+hBVdq9ltuFkifvqkxm83s+GY7K977kLLSMiY99gC3TrwDlVpV47ZKpZLw9u3IPJXuhEiFM1Wb3H/zzTftcpC5c+cyd+7ca96vbvRSx44dWb16dZ2XiZqZjEbOpJ2mx8B+ALbkvpePNyFhrTl9Ms2F0TXcDcOH0btvLw7+dog+/Xu5OhynaRvehjbhbUhMSOKe++922HHMZjObVn5FWEQ4A4YM4udvvufUMUnuNwf2uiYI+zLoLCP3/QMD6j3Sx1qrX+2hxKDT2+oxisZLl50NQJALJtQFUKstCf3ysjKnJ/f79evHnDlzOHHiBB07dmTdunX06NEDX19f0tPTSUxMJCYmhk2bNtGlSxf8/PwYPnw4s2bN4u6776Z169YYjUaSk5OJjq5d2T6rvLw8unbtilqt5tixYyQmJjJq1CjbsvDwcAC++uorSktLAcjJyUGlUjFs2DCGDBnCjz/+yOnTpxtlcl/a+cbJcHk+lIbWYQbLDQLrzQIhAIqLivlxyzZ8/PwYdkv1ZdFE8yLXBOcy6PR26dddPb9KRIP3JhqTS+cu8H+L/8Whvfvp2rsnU/7+NGGXqynUVnj7SPbv2uOgCIWrVJvctz7WLJqWi+cuUFZaSpnRBECXbp1tyyI6RHL6pHvfxVMoFDw940kevf8JBg2Jc3U4ThUT258d//vZofX1Du3dR/rxkzwy6xmUSiXtu3Ri/649MuN6MyDXhMbJkKPD31+JXz1L8gB4+/qiUqlRq5Uy0aKb0GflALh0Ql2A8nLnT6obHBzMwoULmTFjBuXl5QQHB/Pmm2+SlpZmK934+uuvo1QqWbhwIQADBw5k+vTpTJ06FaPRSFlZGbfcckudk/tTp05l5syZbNy4kXbt2jFw4EDbshdeeIEnnniC0NBQYmNj0V6eA+PcuXO8+OKLlJeXYzQaGTZsGH379rXXx2FX0s43TvrLyfiGluWx7COIjBNpDd6PcH9FhYVsX7+VrV+sJc+Qi4+fL0NHDm9wSRDRdMg1wblydTqiOndo8H5syX15SqvJMJlM/PZLAu9//Q8UCgV/nT6VEWNurVfOJ7x9JD9t/c7ypIgd+hWicaj1hLoAycnJJCYmotPpKtRZreoRZ9E4WR/BydYbaB0Wivaq/6EjOkSxb9ceSktK0NRxsuHGJCZuAN/v3kZQsNbVoThVTNwANq7dzInUkw4rzbNp5VcEtWzBkJv+BED7rp0wm82kHz9Jt951S5II9ybXhMbBoNMTEOBPgLb+IzoVCgV+gYHkZOfLDwE3ocu2JPe1Lqq5r748aX1ZablLjj9s2DCGDRtW4b20tDSUSiXz58+vdJsxY8YwZsyYa94fP34848ePr/R1eHg4CQkJtmU9evSwzfv0R+PGjWPcuHG2188++6xtH2vXrq3VeTU20s43DrmXR+4H2qFfqw3Wkqs31Ku8lWgaCvML+HbtJr75agP5uXn0jh1Aqzat2b5+C5fOX6BVWGtXhygaKbkmOI7JZMKQo7fLTVxbWR4ZsNMklJeXs/DvL3Fk/0F6DezPw3+bRsvWreq9v4j2UYAlLyjJ/aaj1rd5vvjiC+655x5+/fVXli9fzrFjx/j444+rnHBMNF7W5H5aeqatJI9VRIcozCYTZ9JOuyI0u2puiX2AAbH9AUvdfUc4/vtRkn87xG1332Ebtdm+i+UmgtTdb17kmtA4mIxG8gy5mE3lDRq5DxAQFIyHRoVByjW4BV1WNgqFwi5lOupDoVCgVqtdMnJfOIe0842HpfyaJ17e3g3eV2BQEMbycgry8u0QmXAn+bl5rPl4BdPvfoiv/u8zOkd35+Wli5m58GWGjhwOQHrqSRdHKRoruSY4VkFunmUupRr6dXt+/I4PXnuxws2VP/Lx80XtoZYBO01E2rETHNl/kCG3jmDmwpcblNgHbOVX3b0ct6io1sn9Dz/8kA8//JD33nsPLy8v3nvvPd555x3U6joN/heNQGZaBi1DW5F2KqPS5D7I/+ju6uq6+46wadVX+Pr7cePtI23vBQYHERzSUpL7zYxcExqHPEMuCsyYTEb8G5rc1waj0ahllI+b0GVlExCkrdXkWY6i9vCgvMw1I/crExMT47Yj5BsjaecbD0OOjoAgrV1G2ltH/0vSp/nI0xv4Yvl/mD7pIdb9ZxU9B/Th1eXv8LfXX6JjN8tvwYgOUSiVStKOnXBxtKKxkmuCY1n73zWNpD7++yEunTtDzsULVa6jUCgIDA6SPn0TkZZqaZe79e9tt36Af2CATKrbxNS6Jc7OziYmJgawzLBsMpm44YYb+Pvf/+6w4IRjZJ5KRxvSAqPReE1yv3XbMDw0Gk6fSnNNcKLBHFV3/0z6aZJ++ZU7/noPXj4VR46179JJkvvNjFwTGgeDTo9abfn/vKEj9/21QahUCgw5OXaITDiaLjvHZfX2rTw8PCgqLHRpDMJxpJ1vPAw59quLe3W5hrZR7eyyT9E4GXJ0bPliHd9v2EJpSSlxf7qesX+52zaY62oaT0/aREaQdlyS+6JyDbkm6HQ6Zs6cSUZGBhqNhsjISBYsWEBwsGv7MY2JNRFfXVkes9nM+dNpAJxJO0GL0KpLaAUGaeVp3CYiPfUEvv5++AcF2mV/CoWC8PbuP9emqKjWmb/WrVtz+rSlVEtUVBTff/89iYmJeFwuzSHcQ3l5OedOn0F5+Xv7Y3JfqVLRNipC/kd3YzFxA9DrDJyw82O1m1etQePpyc13jLpmWfuunTh3OpPCAknyNBdyTWgcDDk61B6WS3lDR+77a7UoFFKf013os3IIauHaH8VqDzXl5eWYTCaXxiEcQ9r5xsOg09uS8g1l3Y9eRu43WbqsbP67ZBnTJz3M16vXEzP0Ot745H2mzZtVaWLfKqpzBynLI6rUkGuCQqFgypQpbNu2jU2bNhEREcGiRYscHbJbsT5NVV1ZnlxdDoX5lpJqmaeqvxEXGBwkT2g1EWnHThDVuaNd58mJ6BDFmbQM6cM3IbUeuT9lyhROnjxJREQETzzxBM888wxlZWXMmTPHkfEJO7uQedZSZ7O4BD8/X9qEt7lmnYj2URzcu88F0Ql7uLruvr0m1c2+eIld3/2PEeNuw1977R3j9l07A5a7yt379rLLMUXjJteExuHqkfsNTu4HWn5MFObpGxiVcAZddg4d/nCD3tmsc68Yy8tRajQujUXYn7TzjYchR0eX6O522Zf1CYBcGdHZ5GRduMjmVWv4acu3mEwmhtx8I2PunUjr8La12j6yc0d++fZ/6LN1aFu4Zj4X0Xg15Jqg1WqJi4uzve7bty+rVq1yZLhux1aWp5rk/vnTlgGYPn5+nEmr/kZcYJCWk0dT7RafcI3y8nJOn0rj5vFj7Lrf8PaRFBcVkXXhokyi3kTUOrmfnJzM6NGjAbjhhhvYs2cPZWVl+Pr6Oiw4YX+nL9fVOn8xiy7dulRatiWiQyQ/b/ueXL3B2eEJO7i67v49999tl31+/eV6AG6beEely9t3tU6qmyrJ/WZCrgmNgyFHj9rDUnPdHmV5AIoLC+1e1kvYV3l5Obk6faMYuQ9QVlaOhyT3mxxp5xsHY7mR/Nw8u43c9/X3Q6VWy8j9JsSQreOjRUvY8c33AAy79SZGT76zzgmbqM4dAUg/fgJtixi7xyncm72uCSaTiVWrVjF8+HBHhOm2LH16NT5+VX+e506noVAo6B07hIT/fUtpSTEaT69K1w0MCiJXb8BkNKJUuW5+JtEwZ9IyKC8rJ6pLR7vu1zqpbuapdEnuNxF1mv3kiSeewMfHh1GjRjF69Gjat2/vqLiEg5xJy0ChVHL8RBpjxo+udJ2IjpbvVSbVdV8D4wbw4/c77JKgyzPk8r/N3zD4phuqnJk9QBtIi9AQTskkXM2KXBNcz6DTo/H0QOPlhaeXd80bVMNfqwVAqYKCvHz8AwPsEKFwBOtj1kEtW7g0DuvI/fKyMpfGIRxH2nnXyzUYMJvN1dZhttr3y4+079aDoJaV99fg8kSLQVopwdZE7Nr+Ix+/8S5qlYrho0YyavKdtGgVUq99RXbqAEBa6kn6xElyX1zLHteEV155BR8fH+677746bXf48OE6HwsgKSmpXts528nU43j5+LBvX9UVFFIOH8I3MIgypRqz2cyP331Li7CKT+ZYzze3IA+zycTOn3/Bx9/PobG7krt8v/X1+579ABSUFuOJn93Ot6S4GICEn3dh9myck2I39e/2jxp6vrX+FufMmcMLL7zA7t272bx5M3fddRcRERGMHj2aBx98sEFBCOfJPJVOi1YtOXT63DX19q0i2kcBluR+SPtwJ0Yn7CUmbgAb1mzi+LETdOnWuUH7+m7dZkqKSxg1aUK161km1ZVH/5oLuSY0DoYcHd7eng0uyQPgH2DZh1qtxJCjk+R+I6a7lA3g8gl11Wo1ChQuSe537dqVffv21XrEYGZmJhMmTCAhIaFOx0lISCA+Pp61a9fWJ0y3Ju1842Atn1NdHWaAPL2OrZ9/yoChN3Lr3X+pdt3AIC25Ohm53xT8vO17AoO1vLL07Qbf8PXx86VVm9akp8pgHXEte1wT4uPjSU9PZ+nSpXUegBYdHY2np2edtklKSmLAgAF12sZVvv9iIyGtQ6uM12w2s+Or/9KhezQ33DSSpO1b8NWoKqx/9fmW5xXxv3VbiWoXWe1cG+7Mnb7f+vp9ZyKeXl6MGPln9u/fb9fz/SI0BFNpeaP8DJvDd3s16/mWlJTU+0ZmnVpUpVLJkCFD+Mc//sHmzZvRarUsXLiwXgcWrnH6VDo+AZaETdcelSf3A4O1+AcGyKS6buzquvsNUVxUzLdrN9F/SJzt0a2qtO/amfOZZynML2jQMYX7kGuC6xl0ejw8VQ0uyQOgUqvx9PLBw0MlIzobOV12DgBBLVw7cl+hUFgm1S0rd2kcwnEa0s7Hx8czfPhwunbtyrFjxypdZ8mSJQwePJixY8cyduxYXn75ZXuG3yRYy+cE1jBy/0y6pf7y6RM1D7QIDA5CLzX33Z7ZbObk0VTCO7W325NcUZ07kibJfVGFhlwT3n77bQ4fPsx7772HRkr5XcOg01fbzucb9BTk5RLWLhIfPz+CW4VypppJdQODLfuSEmzuLe3YCSI7dXBIudSI9lFknpKcX1NRp7+QgoICNmzYwKOPPsrIkSNRqVS88cYbjopN2FlpSSkXzpzDCKjVKjp0qvwxOoVCQUSHKDJPpTk1PmE/bdqG0fZy3f2G+HHLNvJz8xg9+c4a121/uQ6c/CBoPuSa4Hq5Oh0qpcIuI/cBfAMCUXsoJbnfyOmyLCP3tS4euQ9cTu67riyPyWTi9ddf57nnniMtLa3ChH2ZmZkVXoMl4XznnXcyevRoEhMTbe//9NNPTJo0ifHjx3P33Xfz22+/VXq8qtY7efIkd999N2PGjGHUqFF89NFHAGzfvp3Ro0czduxYRo0aVecnB1ytIe38iBEjWLFiBW3bVj+Z57hx49iwYQMbNmxg3rx59gi7SbG2xzWV5TmbdgqAi+fOUFRY/UCLwGAZud8UXDhzjoK8fFpH1G7C3NqI7NyRi2fPy2AdUan6XhNSU1NZunQpFy9eZNKkSYwdO5Ynn3zSCRG7j1ydzpaQr8y5y5PphkVEARDevhNn0k5iNpsrXT8wyPK0l0Fu5Lotk9FI+vGTdq+3bxXePpKzGZmUl8sgnaag1mV5nn76aX7++Wd69OjB7bffzhtvvEFwsOt/VIraO5eRidlkQp+bT/uO7at9rC28QyQ/bfkWs8nkxAiFPcU0sO5+eVkZX3+5nm59ouncs3uN67fvYplU92RKKj369a7z8YR7kWtC42DI0ePj7Yl/oH0mWgwMDkadnmErAyEaJ112Diq12mWlkw4m7OS33b8AUFZaislowtO78gnd6qrv4OvpHTekVuuWlJTwwgsv0LZtW9566y2OHz9e7fp6vZ6uXbsya9Ys9uzZw3PPPcf27ds5f/4877//Ph999BF+fn6kpqbyyCOP8OOPP1bYPiMjo8r1Vq5cybBhw2zJCoPBAMC7777LvHnziImJwWg0UlRUVPcPxUUa2s7HxEjNbnuwJuFrKstzNv0Uag8PysvKyDyRSudefatcNzA4CEOOXiZPd3MnL5fDbN3Ofsn9qM6Wuvvpx0/SvW8vu+1XuL+GXBM6d+5MSkqKgyN0XyaTCUOOvtqJ089lpIFCQWh4BABtozpwMGEn+uxLlc6zYn0KwCAj993W+TPnKCkuJvJyu2xvER0iMZaXc/70mRqrNIjGr9bJ/ejoaJ5//nnatGnjyHiEA2WmWe72ZmSeY8Dg6n9wtevQnpLiErkYuLGG1t3f9f0Osi9e4qG/1W5Uhb82kJDWoaSlVJ9cEU2DXBNcz2Q0kp+fB2jsNnJf26Ilag+VPMLbyOmzctC2CGoUSTmFQoEZM2AGFE499pQpU7j99tt5+OGHa7W+h4cHY8aMASA2NhYvLy9OnjxJUlISGRkZ3HvvvbZ1y8vLycrKqrD9zz//XOV6AwcOJD4+nrKyMuLi4hg0aBAAgwYN4o033uCWW25h2LBhdOlSeUnExshZ7fyWLVv45ZdfCAkJ4amnnqJfv34OPZ670efo8PTyxMun6knTzSYTZzNO0XNAHIf27iajpuR+kBaTyURBbh7+2kAHRC2c4WTyMTSenrQIrd8EupWJ6nzlSVxJ7ourSd/fcQpy8zCZTNWW5Tl/OoOWoa3ReFoGU4S3t/y/eubUyUqT+96+PnhoNPI0rhuzVkSwtsv2Zk3onz6ZJsn9JqDWyf1HH33UkXEIJ8g8lY5KreL8mbNVTqZrFdHB8j931rmLzghNOMDVdffrmtw3mUxsXvUV7TpG0Tu29hOZRHXpyElJ7jcLck1wvTxDLurLuV171NwHCNAGoVYr0efk2GV/wjF02TkEtXDdkzK944bYRtfnGXK5dP4C4VGRaDydW0M3Li6On3/+mXvuuQcfHx9UKlWFx9NLSkqq3d5sNqNQWG5IDB06tNK6wSdOVCw1V9V6I0eOpG/fvuzcuZPly5ezZs0aFi1axOzZs0lJSeHXX3/lmWee4cEHH+Suu+6qz+k6nTPa+UmTJvH444/j4eHBzp07eeKJJ9i6dStBQXV7Gqm+k48lJTWsfKEzpB0/iaePT7Wx5utzKC0uxqT2JCA4hOQD+9FGXDvSz7qP7MtPA+z6ZSctw0IdE7iLucN321AHE/fTMiwUpUpl1/P1DfBn3697adUhwm77tLfm8P1erb5tnD1J399xrAn46pP7aUR26WZ7HRLWFg+NJ5mnThA9cNA16ysUCpk83c2lHTuB2kNN26h2Dtl/m3YRKJVKMk9lOGT/wrlqndxviPj4eLZt28aZM2fYtGkTXbp0QafTMXPmTDIyMtBoNERGRrJgwQLbo13Dhw9Ho9HYSsfMmDGDoUOHAnDq1Cmef/559Ho9Wq2W+Ph4oqKinHEqbi3zVDqBLYIxp9ec3G8bFYlCoSDr3AUnRSfs7eq6+5P/OqlO2+7ftYez6ad54sW/2xIftdG+a2f27thFQV4+vv5+dQ1ZCFEHBp0etYcKwG4j9/21loRabk62XfYnHEOXlU2bduGuDgOwjIYHKC8vc3pyf9q0aaxYsYIpU6awbNkyWrRoQVlZGenp6URGRrJ58+YK65eVlbFp0ybGjh1LYmIiJSUltG9vKVP4r3/9i9TUVDp3ttwMP3jwIL17VywxN2TIkCrXS09PJyIigvHjxxMZGcns2bMBSy3+rl270rVrVwoLCzl06JDbJPedISTkyojjIUOGEBYWRmpqKrH/z955hzdVtn/8k53upHs33RTKBhFBVERRQXHhQFyvW1/9uV4nirhecb7ugRPBhQsFFMQJiIyyR+lM987qTLN+f6Qto4OOtEnL+VxXr6ZnPM99cpqTc77P/Xzvk07qUTvp6eld2k12REZGBuPHdz+BwV2sW/4toeGhXca6+59NAEw5/Qx2K+Vs+X0do0aORHZE0cojj9dbImfNshVERUSSPn5Mv8bvDgbLue0LVquVN8sqmH7BOQAuPd6k4anUVFR57Ht4IpzfI8nIyCA9Pd0jBH6B/uF4tVXqTEZqjYY2v30AsURCpCaeEm1XRXWF4umDmYLsXGLiNUil/SPbyuQywmOiKBJqbQ4JBkTcP/PMM7nmmmuOmsYsEom48cYb2wqdLV68mBdffJFnn322bZvXXnutw+nLCxcuZN68ecyZM4eVK1fy+OOPs3Tp0v4/kEFOsbYQmdI5jet4mdxKLyUhEeGCuD/I6Y3vvsPh4IflKwiJCGPSaVN71F98qtN3Pz8rZ0g+LAoIeBJGvQFpS+q+qzL3W737a00Gl7Qn0D/oq2s8praJVOa8lbRa3FOM6+abb0apVHLdddfx2muv8eijj3L99dcTFRXVrpiuSqWioKCAuXPn0tTUxMsvv4xcLkej0fDCCy/w6KOP0tTUhMViYdy4ce3E/a62++mnn/jxxx+RyWSIRKI2cf+ll16ioKAAiUSCv78/zzzzzIC9N4OBiooKwsKcmeMHDx6kpKSE+Ph4N0flWRj1BsKiIrrcprQgD7lSSVBoOLGJyWxe/xMl2jw0R2R5HklrdqhJsGsYtJTkF9JsNpM4zPVWX5rkRPZu3UGz2Yy8h4NmAgICPafVCrmz2irlLcV0w2OPtk6J0iTyz/qfsTQ3HzWY20pAoIrqckHPGYw4HA602bmcdFr36lD1luj4OLRZgvPCUGBAxP2OCmqpVKqjHrrGjBnD559/fty2ampqOHDgAB999BEAs2fP5qmnnkKn0wnFHLugqaGRqrIKAiIjCI8IQ9XFlK9WYhLi2go1CQxOJp48oce++5m795F78BDX3XM7EqmkR/21FtXNPySI+wIC/Y1Rp0cqc4r7rsvcd7bTWF/nkvYEXI+5qYmGunrUwZ5xzyORShEhwmqxDGi/Rxbmu+aaa7jmmmuor6/n0ksv5dJLL21b9+9//xuA6OhotmzZAjiLAh7L1KlTmTq1/YD2pEmT+Pbbb4+73a233sqtt97abvmbb77Zg6MaWjz99NOsW7eO6upqrr/+elQqFatXr+amm27irrvuYuTIkbz88svs378fsViMTCbj+eefPyqbX8B5rU9JT+tym1JtHpGx8YjEYqITk0Ekoig3q3Nxv0VAEuqrDF7yDmUBkDAsheLyUpe2rUlOxG63U5SnJTEt1aVtCwgItKfNlqcTcb+sVdyPOtqeJTo+EbvdRlmRltjE9gN9AWoVOQeEQsaDkZqKKupr6/rNb7+VmIQ4tv6xkabGJpReyn7tS6B/GRBx/3jY7XY+//xzpk+fftTy+++/H4fDwfjx47n33nvx9/enrKyMsLAwJBKn6CiRSAgNDaWsrEwQ97ugpMDpo1VZrTuuJU8rsYnx7Ni0RcjaGMT0xnf/x8++xl+tYto5Z/a4P19/P0Ijw8kXRn8FBPodo86Zua/09kHaYo3SV1pteSzmph7N+BEYOAw1LdldQUFujsSJSCRCKpMOuLgv4PksWLCABQsWtFu+ZMmStteLFy8eyJAGHTarjTpTbaeCD4DVYqGipJiTz5wJgJe3D6ERURTmdp6g4+3rg1QmFTL3BzG5B7Pa7rtdLe7HJTvrNWiz8wRxX0BgADDqDEhlUrx9fTpcX16oJTA0DIXX0YXVozTOz2pJfm7H4n6gmlqjCbvNhljSs6Q9AfeSn+3UU+L6WdxvLaRboi0QrveDHI8Q95966im8vb2ZP39+27Lly5cTERFBc3MzzzzzDE8++SQvvviiS/sdysW3jmX/1p0AFBWXMWxsereOodlhw+FwsP6ndYTFRPZ3iB7DYDy/XRESGswva9eTmt5e3D/2WCtLytizNYMp553J3l5+PgJCgsjcs98j30dPjKk/Ebw5hzZGvQG5Qoa/qmeFJ7tC6eWNWCxBIhFRX1uHX4C/y9oWcA36Gmc9hMBgzxD3AaQyGRY32fIICAxlTEYjDoejS3G/vLgQu91GpOawnVFMYjJ7t/7dqaDjLLSoFjL3BzF5mdnEpyb3qDZWdwkJD8Pb1wdtdude3gICAq7DqNPjr1J1+nkuKyogOiGp3XIfP39UwSGU5Od1uF+AWoXDbqfWaOrye0TA89Bm5SIWi4lN1PRrPzHxzvaL8wVxf7DjdnF/8eLFFBQU8M477xyVIRgR4fSWlMvlzJs3j9tuu61teUVFBTabDYlEgs1mo7Kysm37njCUi28dS+aWnUhlUposFk6fflq3jiEiJIzVS7/CV+E1KI+5NwzW89sVU6adwm+//MHYsWOP+ox1dKxvrH4epbcX19x2U68L4pZl5fPF7o9JTU7B19+vT7G7kqF4brtCKL419DHp9cgVMpf57YNT8FF4eSOV1mPSGwRx3wPRVznFfU+x5QGn735DXb27wxAQGHK0+jAHdGGnWVrgFHWi4hLalsUmppCx4XfKiwuJjOu4hkFAoAqjUGhxUGJuaqI4v4BxUyYdf+NeIBKJ0CQnUiCI+wICA4JRb+hUfK+vNWHS644qpnsk0fGJaA8dxOFwtFt3pAWbIO4PLgqy84iMje53B43QiDDkCgVFeQX92o9A/+PW+favvPIK+/bt480330R+RAGQhoYGamtrAWchiTVr1pCW5vSaDAoKIi0tjVWrVgGwatUq0tLSBEue41CcX4hPQABAt215wqMikEilQvXsQc6ESeMxGU1kH+raKqeipIwtf2xkxpzzei3sA2hSDhfVFRDoiMWLFzN9+nRSU1PJysrqcBubzcaiRYuYMWMGZ511FitWrBjgKD0fg86AVCpymd9+Kz5+/khlYiGj00PR1+gAUAV5zn2PTCbDZrNht9vdHYqAwJCiVXzvWtzPx0+lbrNVA4hJct7rF+Z0/B3b2qZRuM4PSgqy87Db7SR003KzN8QlJ1CUq8VqFWZlCQj0N0a9odPrfHmR0145PCauw/VRmkTqTEZMel27dULx9MGLNjsXTUr/WvIAiCUSojSxFOcL4v5gZ0DE/aeffppp06ZRXl7O9ddfz6xZs8jOzuadd96hsrKSK664gjlz5nDHHXcAzqK5V199Neeffz6zZ88mPz+fhQsXtrX3xBNPsGzZMmbOnMmyZctYtGjRQBzGoKY4vwAkEnx9fYiM7p7FjlgiISg8pFujeFaLhdyD+zA3NfY1VAEXc6Tvfles+fJbJBIxMy+d06f+DhfVFYoxC3TMmWeeyfLly4mKiup0mx9//JHCwkLWrVvHl19+yeuvv05xcfEARun5mPQ6RCKHSzP3AfzVgUhlkrbiXgKehaFah1yh6NSX1R201nwQfPcFBFxL63XYvytxX5vfLjvfX6VGFRRMUW4X4n6gWrjOD1JyM1uK6aZ2L2GrN8QlJWKxWCgrFO69BAT6G5NeT0CgqsN15S3FdCM6Efej450CcIm2/UwboXj64MRQo8dQo0OT3N6KqT+Ijo+jKE87IH0J9B8DYsvTWUGtQ4c6rtwdExPD999/32l7iYmJQhZnD6ivrUNfXYPY15eUYSk9KpAYHBFGUU5+p+vLiwrYtXkj+7b/Q1NDPaecPYvpF1ziirAFXEREZDjRMVFs37qDq667ssNtDDV6/vppPaeeMwN1H7NBffx8CY2MIP84MwUETlwmTJhw3G3WrFnD3LlzEYvFBAYGMmPGDH7++WduvPHGAYhwcFBrMOLtJXd55r46OASpVIyxRngQ8ET0NTpUQYH94rPcW6Qy5+2k1WLt9+nDAgInEiZ9SwHtTuwUGuvr0FVVMHry1HbrYhJTyD2wB4fD0eH1IkCtptZgFAotDkLyMrMJCg1BFdR/NhvxLRmj2uxcYhI0/daPgMCJjt1ux6jr3JanrEiLOjgUpbd3h+tDo6KRyuQU5+cSpDl6wK91wEAYyB1cFOQ4B2pai5v3NzEJcWz4eT0mgxF/VcCA9CngetxqyyMwMJRonVO5Ssoru23J00pwRBhGvQGTwdi2rKGujm1/rGfJc0/w/uJF7Pz7TxLSRhAWFUP23l2uDF3ARUyYNJ6MrTs6tUxY+81KrDYbsy6/2CX9JaQmCeK+QJ8oKysjMvLwLKOIiAjKy8vdGJFnYbfZaGx0epz7ubCgLkBgaChisQh9dZVL2xVwDfrqGo/y2wchc19AoL8w6PQolAqU3l4dri8t0AIQGddeAIhNSqGhro6airIO9w0IVGG326k11bosXoGBIfdgVr9a8gBExEQhVygoyO64UKeAgIBrqDfVYrfbu7DlKSA8JrbT/SUSKRGxmg6L6iq9vJArFIIF2yBDm9Ui7icNkLgf75wVIljzDG7cXlBXoP8pavmQmurqey7uh4cBUJiTh5e3jN3/bODQnp3YrFbCY+I457L5jJgwCS9vH7b8to5fvv0CfXUV6uAQlx+HQO+ZMGk833/9A9mHctr9DzTU1bN+5RpOOm0K4d20bDoe8anJ/PP7BmoNRvyE0V8BN9KXgsIZGV1bWbmT+to6pBJnJmZxaRl1lr55nR95rFUtDwD5uTke/R70hcF8XOUlZYRFR/ToGFxxvFKplPr6zovmihDR2NiIRC7rc199oasYhwLNzc1Hnc/B/L8scHxMekPXljwFeSASERmrabcuNvGw735wePv7u9YsUVMXXs8CnkedqZbK0jLOmD2zX/sRSyTEJGjQCkV1BQT6ldas+o6uw431dRhqqhk39fQu24iOT2DrH+sZYbMdtVwkEgnF0wch2uxcwqIiBsyCM/oIcX/42FED0qeA6xHE/ROA4vwCpHIZFpud1OE9E/d9/LwIDvVh9edLsJib8PL2YdzU0xl98lTCo48eQU4eOZpfvv2CnH27mXj6DFcegkAfmTDpsO/+seL+rz/8RGN9A7OvdJ2dUnzq4aK6o04a77J2BU4cIiIiKC0tZdQo5w3GsZn83SU9PR1FL2xCMjIyGD/ec/93C3PzkUqdk+8mnjwZ/z5k7x97rMXqAHb9vhYJDo9+D3qLp5/brnA4HLxVV0dCSlK3j8FVx3vw4EF8fDp/yJDKZOCgy21cSWpqKvfccw+//PILBoOBBx54gKlTp+Lj48Nff/3Fyy+/jM1mIzAwkCeffJK4uDi2bNnCs88+y+jRo9m5cycikYhXXnmFxESn/cR3333HZ599hs1mw9fXlyeeeIKEhIHJmuoucrmc0aNHA4fPrdls7tNApoDnYtQZOrXkAWcx3eCwCBRe7TP7A0PD8PHzpzA3u0NhqFVIMuj0gu3KICKvxW8/sYcJW71Bk5LI3+v/wG6398jWVUBAoPu0+uF3NJBbXtxSTDe6Y7/9VqLiE7Gt/xlTTftZtwFqlVBQd5Chzc4lPrV/Z2cdiSooEF9/v7akYIHBiSDunwCUaAtR+vgglUpISIo/7vaWZjMHd2Wwe/NGCrIzCQrxRiSScckN/yI5fXTb9PtjCQwJIygsnGxB3Pc4wiPCiYmNbue732xu5ucV3zNy4ti2QriuQJPsFErys3IFcV+gV5xzzjmsWLGCs88+G4PBwPr161m+fLm7w/IYjHoDUpnTI9nXz9+lbfupnZYvjfWCVYOn0VjfgLnJ3OfaKH0lL38/eXlHi8nNZjMOQJHZN8/9hIR0EuJHdGtbX19fvvnmGzIyMrj77rv5+eefqamp4YEHHmDZsmUkJSWxYsUK7r///rZaTTk5Ofz3v//lySef5O233+att97ipZdeYvv27fz0008sX74cuVzOn3/+ySOPPMIXX3zRp+MREOgLRp2esE5mVTocDkoL8kkcPrLD9SKRiJjE5E6L6rZm7gsZnYOL3MwsRCIRGhfet3eGJjmBX1euoaqsgrCoiH7vT0DgRKRVeO9oILes0Cm2hndSTLeVqBZrNkNVewvTgEA1FSUd27MJeB71tXVUlVVwxuxzBqxPkUhEdHwcxUJR3UGNMAR/AlCcX0CzzU58YnynGawOh4MSbS6rP/+E/z1yLz8sfR+TXkfKuEmI5aHU1YlJGzuhU2G/leQRoynIOYS5qbE/DkWgD3Tku79h7a8Y9QbOnze3V21u3byNz5d+ia5Gd9Ryb18fwqMjyT+U3aeYBYYmTz/9NNOmTaO8vJzrr7+eWbNmAXDTTTexd+9eAObMmUN0dDRnn302l112GXfccQcxMTHuDNujMOoMSKVivHx8XV4I0dffOVggXMc9D33LtVYVHOTmSNojEolx2B0D2ud5550HwJgxY6isrMRsNrN7926GDRtGUpJT+Lrkkks4ePAgdXV1AMTHxzN8+PC2/YqKigD47bffyMzMZO7cucyZM4eXXnpJqPMh4HaMXVjmGPU11NeaiNJ0PrskNjEFo64Go17Xbl1ru4IX8+AiLzObiNhovH06Lq7pSuKSnMk6BYI1j4BAv9Fmy9OBuF9epCUgMAhvX98u2/BTqfFXB2GorGi3TsjcH1y0WqG1FjUfKGLi4yjKL8DhGNh7eQHXIWTuD3Fai+HW2hyMPHlCh9vs3bqZTetWU11eikwuJ23sREZPnkpsYgo7duzAXC/ij9VruzUlM3nkGP75bS35mQcYNkbI2PYkJkwax3crVrb57tttNlZ/8Q2JaSmkjek466szDHoDLz77Cqu+XwPAy8+9ylnnnsllV13K6LGjEIlExKcmk7X3QH8cisAgZ8GCBSxYsKDd8iVLlrS9lkgkLFq0aCDDGlSY9HqkMnGf7Hg6QyKRIpHKsVlMwlR8D0NfXQPg9sz9hPgR7bLrDTU6dNU1aJISXD7g1BmtCQuSlv5sNhsOhwORSNTpPnK5vO21WCzGarUCziSHSy65hP/7v//rx4gFBLqPzWqjzlTboeADUKp1Fk+MjOt8Vm5MktO6pSgnC8RHJ+h4+Xgjk8vbhCUBz8fhcJB3MGvAZsVGx8chkUjIz87lpNOnDkifAgInGkadAalM2qG/ellRARExmm61Ex2fSN6h9s/eAYFqao0mbFYbEunh+7OS4lIqyioYN3Fsr2MXcD2tg6lxyQMr7kfHx9HU0EhNRRXB4aED2reAaxCe2Ic4JVqnT1u13tBhMd36WhM/fPo+YomYWfOu4+5nXuGCq28gLim17eE4JkGDuclMZenxM9iiExJRenmTvW+3aw9EoM+MP+mw7z5A9p6DVJaWM3vepV0KIUficDhYu3odF51zGT+vWsvNd9zAVz9+xqVXXsxfv23gustv5Io58/nmy++Ijo+jprJKeGgUEOgHDDoDMpkEf3X/iLwKpRcSqYj62rp+aV+gdxhaMvfVwe4V9zuidWZfq1juLsaOHcvBgwfJzXU+HH333XcMHz4c3+NkvU2fPp2VK1e2ZevbbDbBx17ArZiMRhwORxfifj4SqZTQyOhO2wiLikGuVFLYgTWPSCRqyegUMvcHC7qqaox6AwnDBsaLWa6QE6WJoSA7b0D6ExA4ETHq9ASo1e2ex5saG9BXVRIe27UlTytR8Qk01ddhMhx9TQ8IVONwODAZjUctf+Lhp7jhqlv489e/+nYAAi5Fm51LYEgw/qqAAe23tfaO4Ls/eBHE/SFOccuH02yxdiju5+zfi8Ph4Pyr/sXYU6Z1WJArJsH5hVKUrz1ufxKJlITh6eTs34PjCPsXAffT6ru/bUsGDoeDbb9tICImmvFTTu7W/hVlFdx96308ePejREZH8Nl3n3L73beSMiyZBx+7n3Ub17DgqYdxOBw8teBZXnv1XQC2/LmpPw9LQOCExKTXI5NL8AtwfeY+gLevP1KZRJjG62Hoq1tseYI8z5anVdy3WCxujSMwMJDnn3+e+++/n/PPP58ffviBF1544bj7TZw4kbvvvpvbbruNCy64gNmzZ/Prr78OQMQCAh3TapfTmS1PSUE+4dFxSKSdT8QWi8VExydRlNuxTWJAoBqD4Lk/aMg96BykSRiAYrqtxCUlos3OFawaBAT6CaPe0HEx3SJnkmbEcfz2W4mOd2Z6t87qaqX1O8R0xLW+sqKK7VsykEqlPHTvAjIPHOp54AL9gjY7j7jkzu32+osoTSwARYLv/qBFEPeHOMX5BUjlcqx2OykdZHlk79uFX4CqyyItUZo4RCIRxXndG8VLSR9Dfa2J0sL8Xsct0D9MmDSeHdt2sntrBlWl5cyed8lxLTfsdjtff/4tl5x3OVv+3sp9D9/N0q8+bPf/5O3jzaVXXMyXPyznoy/eZ8IpJ+FwOHjhyRe45drb+W3d727P6BQQGCoYdHrEYhG+KlW/tO+nUiOTioWZNx6GvroGbx8flF5Kd4fSDqnMKTBaLQNznT906BA+Pj5H/e3t7fSgnjZtGt999x0//vgjn3zyCXFxznucSZMm8e2337btc+zfF1xwAd999x0//PADP/30k2DRI+BWWgvddiTu2202you0RGk6t+RpJTYxmaqyEpqbmtqtCwgUMvcHE3mZ2UikUmITjn/eXYUmJRGT3tA2c0xAQMC1dFZbpbxICxy/mG4rYVGxiMRiivOPrpHROvvLcER9lV9+Wo/D4eDND17F39+Pu26+l8qKqt4dgIDLaGpsoqywGM0AW/IA+Pj5EhQa0pYcLDD4EMT9IU5xfiESuYzwiDBUx3xpWC0W8g7uIzl9TJe2LEovJaGR4RTmdk+sTxyejkgsFqx5PJAJk8ZhMppY89X3ePn6MGXG6V1uX5BfwE3zb+Xpx//L8JFpfL36C67+11Vt/sYdIRKJGDt+NItf/S9hUZEMT0umIL+Qe+94gPNOn8N7b7xPdVW1i49MQODEorZlyq1fgKpf2lcHByORiqmpEm70PQl9dQ0qD7TkAafvvVgkwurmzH0BgaFCV0UWq8pLsTQ3Exl3/Oy+Vt99fWVZu3UBapWQuT+IyMvMIi4pHplcdvyNXURrBqlWsOYREOgXTHo9AYGqdsvLigrxU6nx8fPvVjtSmYyAoFBKtMeI+63F049I2Fm7+hdShiVz0uSJvPbeK9TV1fF/t9xLY0Njbw9DwAUU5ubjcDjQpCS5pf/o+FhB3B/ECOL+EMbhcFCcX0B9k7lDS56CnEM0m80kjxx93LZiEjTd9t/y8vElJiGJ7L2CuO9pjD9pHCLg0J59JI5IbbNROBaLxcqH737M3NnzyDqUwxPPPsa7n7xFdGznvq4dkTQ8Fbu5mdW/r+R/77xIYnICb736LudMm82Ddz9CxtYdwjRfAYFeUG8yAf0n7geFhQFQU17RL+0L9A5Djc7txXQ7QyQSIZXJBixzX0BgqNOVLU9pgTPhJrIbmftRcQlIpFL0FaXt1rUWWrTbbH0LVqDfsdvt5B3KJmHYwFnyAMQmOsX91iKPAgICrsNut2PUGTocxC0v0hIRq+lRe6rQMMoKtdiOmC3fJu63fKeUFJWwZ9dezpl9NgCpaSk898ozHDqYxSP3P45dsFZ2G63XWY0bbHnAWVS3tLBIcFsYpAji/hBGX11DQ3091bqOi+lm792FVCZHk5J23LZiEjRUlJTRbDZ3q+/k9NFUlBRh1AtTOD2J8IhwNDGR2CxWkkZ2fN4P7s/k6kuv47UX32TaGVP59qevuHDuBd0uunsk8alJ6KtrqDWYOP3M03j7o9dZ+cs3XHnN5fy94R9uuOoW5s6+kq+Wf02tULhTQKBb2G02zGZnZk1/ifvBYeEAGKor+6V9gd6hr9Z5ZDHdVqQyqZC5LyDgIox6PQqlAqV3+3pYJdo8lN4+qINDj9uOVCYjIlaDrqLjzH2H3Y7JYHJJzAL9R1lRCU0NjQMu7nv7eBMeHYlWEPcFBFxOvakWu93ebhDX3NRITWVFt/32W1GFhGO1WKgoKWpbpvT2QqFUYmyxYFu3Zj0AM887q22baWdM5f5H7uH3X/7g1Rfe6OXRCPQVbXYufgH+BIYEu6X/mAQNVouVipL29wsCno8g7g9hWqfUNJib24n7DoeDrL27SUgbgUwuP25bMfFxOOx2SrRFx90WnOI+QI5gzeNxRAQHYXc4iErUHLW8qamJV194nfmXXEd1VTUvvbGYF99YTEho779c4lOdU8rys3LalsVpYrnv4XtYt3ENTzz7GDKZjGefWMy08dO5cOalPHzvApZ+sIxt/2zHZKrtdd8CAkOVWlMtUolzsM23n8R9f7VTQDYZBC9mT8HhcKCv0aEOdl8x3ePNtHJm7gvifn8hzHQ7sTB1UmQRoLQgj8i4+G4nXsQmpmCqrmqXpNOaLWo8wnff4XCw5oefKS8r713gAv1CXqazmG5iBzXU+pu45EQKBFseAQGX02a/dsy1vqK4CByObvvtt6IKcc68LWnnu6/C1NLXz6vXMXJ0OlExUUdtc+U1l3PZVZfyyfuf8s2X3/WoXwHXoM3KRZOS2KukSlcQHe/8fxOK6g5OBHF/CFOsdVZYN1ut7cT9ytJiTPqaNhH+eEQnaIDuf9CDwiJQB4cKvvseht1mw1xbi6nRTHnpYbuN7VsyuOz8q/jovaVccPFsvv3pK86cOb3P/WmSnF9O2iPE/Va8vJRcOPcCPvtuKZ+u+Ijb7roZTYKGndt38fJzr3LT1bcxbfx0zj/zIv5z18N8+O7HbN64BYNQ4FPgBMekNyCViRGJxHj7+PZLH34qp+BTXydkc3oKdUYTNqvVbeK+UqmkpqamS4FZKpNhs9uxCRYfLsfhcFBTU4NS6XnFlAX6B4NOj6oDq4Zms5mq0hIi47pfVDU2KQWHw06J9miBtlVQMh1xb7Xtn+08ct9jzLvoWnbv3NOr2AVcT97BLJTeXkQcI8gNBHFJCVSVV1AvzLIVEHAprUVuj7XlKWsppttTWx4vXz/8VGqKj73WB6ox6PTk52o5dDCLmbPOarevSCTigQX3MWXaZP77xGL+2bSlR30L9A1Ls4VibSFxSQNfTLeVyNiYlqLMgu/+YETq7gAE+o/i/AIkMhle3t5ERkcetS5r7y6Abov74VERyORyCvO6V1RXJBKRnD6KjI1/0Gw2I1coehS7QP+Qtf8g5sZGTE1NHDqQRe3Zdbz6/Ot8/cW3RMdE8e4nbzLplJNc1p/S24uI2GjyDmV3uo1IJGLkmHRGjklvW6ar0ZN5IJOD+zM5uP8QB/Ye4Jef1retj4gMJ23EMNJGDGPYiGEMTx9GkBuzWQUEBhKDTo9UKsbLxweRuH/G6BVKL0CEubGhX9oX6Dm66hoAVG7y3I+Ojqa4uJiqLoosm5uaqDWYqDc3IZUN/C1mc3Mz8m7MRhysKJVKoqN7VvtGYPBi0hkIO+b+HaC8qACHw0FUD8T96HjnTMqi3CziUw/bMrYWcWwVmAC+/uJb/AP88fHx5sarbmXhswuYfeF5vTwKAVeRm5lFfEoSYolkwPuOT3GKTdrsXEaM696zo4CAwPExdZK5X15UgK9/AL7+AT1uM0qTQEn+0Yl1AWoV5UUlrF3zCyKRiLPOndHhvlKplMX/e5brrriR++98iKVffUhCUve/awR6T7G2AJvViibFfeK+XCEnPCpSyNwfpAji/hCmOL8Am0hEyrAUxMcIQNl7dxEZF9/tLwyxREK0JpbivO6P4iWlj2brH+vRZh0kZeSYnoQu0E9s37AZqUxKQEgIG37fxK9r/6CmqoZrbriK2/7vVry8XJ8RmJCaxL6Mns3gCAxSc8qpkznl1Mlty0xGE5kHDnFgXyaZ+zM5eCCT3375o219SFgIacNTOff8czj3/JmuCl9AwOMw6gxIZRJ8enHD311EIhESqYymxqZ+60OgZxhqnDVs3OW5L5PJiI/v+gEvLzObF+95jHueXsDIqScPUGSHycjIYPRoQXjyFBYvXszatWspKSnhxx9/JCWlvVe4zWbj6aefZsOGDYhEIm6++Wbmzp3rhmg9D6PeQMrI4e2WlxY4MzIjNd0vuKf09sYvMIjC3Kyjlh+25TEAoKvR8dsvf3DF/Mu48bbr+c9dD7PgPwvJzc7l3/fejsQNwrKAM6OzMDefcy6d45b+45KdYlOBIO4LCLiUNlueYzP3Cwt6nLXfSpQmkcxdGdSZjG1aT0Cgmszd+1i7ah3jJo4lLLzzei2+fr689t4rzL/kOu686W4+/fpjAoPazyITcC3arNZiuu4T9wFiEuIoyBFs2AYjA2LLs3jxYqZPn05qaipZWYdvKvPz87n88suZOXMml19+OVqtts/rBJzYW/zxDabadpY8dSYjpQX5PRbcoxM0PRrFi0tKRa5Ukt0yS0DAvTgcDjI2/MOI8WOYcPJ4SopKUakCWLriQ+596O5+EfYBNClJGGp06FuyTnuLf4A/J02eyHU3Xc1z/3uGleu+YeOO3/lg+bvc/8g9TJo8kfy8Ah6+dwEvPvuKYAshMGQx6Z2Z+wGB/TtbRa7wwuGwYbfb+7Ufge6hr24R94M8d5ZSSMvDYlVZxXG2FDgROPPMM1m+fDlRUZ3biPz4448UFhaybt06vvzyS15//XWKi4sHMErPxGq1UmeqbSf4AJQW5BMQFIyPn3+P2gwMi6QkPxebzdq2TOnlhVyhwNiSub/y6x+xWqxccvlFqNQq3vrwdeZeeQkfvbeUe2//D/V19X07MIFeUZiXj9ViHfBiuq34qwJQBwehFXz3BQRcijNhR4q3r0/bsmazmZqKsh777bcSHe8Uh0uPsOYJUKuoM9WSn6flnFlnH7eNyKgIXn3nJaqrarj3jv9gPqZei4DrKcjJQ+ntRWhkuFvjiI6Po7K0XEjwGoQMiLjf2c39woULmTdvHmvXrmXevHk8/vjjfV4n4KS6ohJzUxP1jeZ24n6rD35yD8X9mIQ4jHoDJoOxW9tLpFIS09LJ3rdbKALnARTm5FNVXsGEqZO55d83cv2t1/DZt0tJHzWiX/tNSHUW/srvwHe/r/j6+TL+pHHMv34eT7+wiO9+/oorr7mcZR99xv/dch91gjeowBDE2OK5rwrqfbHr7uDl44dUKhY8dj0EfU2rLY/nZk/5Bvij9PKiqlwQ9wVgwoQJREREdLnNmjVrmDt3LmKxmMDAQGbMmMHPP/88QBF6LrUGIw6Ho0Nxv0Sb1yNLnlbUYRFYmpspLypsWyYSiQgIVGHUGbDb7Xzz5XeMP2kc8YkaAGQyKY8sepCHFv6HjX/+zbWX30BJcWmvj0ugd+RlOu0tE9xQTLcVTUoi2uzc428oICDQbYw6PQFq9VEFVCtKinA4HET0UtwPj4lDLJZQfERR3VbbH4VM1u26eiPHpPPUC0+wK2M3Tzz8lKDn9DParBw0yYntHDcGmpgEDQ6Hg9KCIrfGIdBzBuQ/p6Ob+5qaGg4cOMDs2bMBmD17NgcOHECn0/V6ncBhWotgNFmtpA4/Rtzfuwt/dRChkT3zbY3pYVFdgKQRo6kzGSkvLjz+xgL9yvaNmxGJxYybMonwiHCmnn4KMrms3/uNTUpAJBaTf8j14v6xSKVSHnzsfh5d9BCbN/7jfAgtKun3fgUEBhJDdQ0SiRi/AFW/9uMboEIqFWMUvl89AkO1Dn9VAFJZ/1+3e4tIJCI4PJTq8kp3hyIwSCgrKyMy8rCvfEREBOXl5W6MyDMwduLDXGcyYtTV9KiYbivqMOf7XHSsNY9ahVGnZ8vf2yguKuHSKy4+ar1IJOKK+ZfxxvuvUlFeyfyLr2XHtp097l+g9+QdzCJArSIoNMRtMWiSEykrKsHcJGRzCgi4CqPegP+xfvuFWgDCe2nLI5PLCY+JPaqAun9LfZWx40b2yGLn7HNncOd9t/PTj2t59433exWPwPGx22wU5mrdbskDzsx9QCiqOwhxm+d+WVkZYWFhbd6NEomE0NBQysrKcDgcvVoXGNgzH9p9+/b1KvaMjIxe7TeQbN24GQCr3Y7RZGiL2Wa1knNgH9HJaezYsaNbbbXuW1/nzN78+88NNDmsXe3ShtnmHOH9c+0akse6rlBrfzIYzm9v2LDuNyI1MeTkHR7FH6hjDQwNZtfWDDSjhg1IfwmpGu5+6E7e/t97XD5nPv++71aShyUN2XPbGb29xgl4Nka9U2zvb3FfHRRMoVhEZVk50fGafu1L4PjoqmvcVky3J4SEh1IliLMCA8xQu6fXtmRql1dWHBVjZZEWgNomS49jV3r74O3nz56MbchUh0Vih1hEeWkZ77/9Ab5+vqhDAjpsW+4l5aEn7ue1F97ipqtv4+ob53HqGVN6cXQDg6ee296wf9ceAiNCu3x26+/jtYocOOx2flnzMxFxMf3aV3cYSue3Owj39EMTo96A+ph7u7KiAnz8/Pt0nx+lSWDX5g3YbTbEEgk11U7rtbG9qJnxr1uuQ5tXwDuvvUdMbDSz5pzb67gEOqasqIRms5m45O7X0ukvwiLDkcnlFOVr3R2KQA85oQvqpqeno1AoerRPRkYG48eP76eIXMeWn35HJJWiSYzn5MmHi9pl79uN3WZlyplnkZiWftx2jj3eL1TvgcXWo/cg658/qddVDYr3bbCc355SUVJGdVkFV91xY9vxDeSxbh89kj3bdjBu3Lijph32J+PHj2faaVO58+Z7efGZ/3HNjVdx173/HpC+PYGMjAzS09OFh4EhSJ3JiFLhzKzvTwJDnf7p1WWCBYMnYKjWua2Ybk8IiQgnc89+HA7HgF3vBQYvERERlJaWMmrUKKB9Jn93GWr39PWVTiHmpMknExZ1ePbzH2WFiMRiTptxNvJeHG/S8JFk79vNuLFjEbVM/d/9x2YqCkvYlZXPVdddycknd1EMezycOm0qD/zfI3z87qfYLDbufuAujyu068nntqc0NjSgr6zmjFlnd3pMA3G8cVEx/PjxFyjEMre/t0Pp/HYH4Z5+6GLU6dtla5cXFRAeE9ene6io+ES2/fkrlWUlhEfHsj1jFwCa+NgetyUSiXj86UcpLSnjiYefIjI6krHjhcLariTfQ4rpAoglEqI0MULm/iDEbYZOERERVFRUtBW9tNlsVFZWEhER0et1Aocp0RbS1Gxp77e/dzdyhYK4pNRetRudENcjWx6A5PQxlBVqqTUaetWnQN/ZvsE5k2PC1Mlu6T8+NQmjTt/noro9JS4+jmVff8T4iWP58O1PePWF14XioAKDnsZ65yyq/s7cD20R2HRVVf3aj0D30NfoUHlwMd1WgsNDaaxvoEEovCnQDc455xxWrFiB3W5Hp9Oxfv16Zs6c6e6w3E5ntjylBfmERkT1WNhvJTYphcb6OqorytqWBQSqqTPVYrPZuOSKi47bhn+AP2+8/z+uvPpyPv3wM+66+R5qhdos/Ub+oRwcDgcJqe4ppttKUFgIvv5+FAi++wICLsFut2PSGwhoscwBsDQ3U1Ve2utiuq1EaZwisbOIuo0/f98IgLmhsVftyeQyXnpjMRFREdx72/0UFwqF711JQU4uMrmcyFj3z4oCpzVPUZ4g7g823CbuBwUFkZaWxqpVqwBYtWoVaWlpBAYG9nqdgBOb1UZJQRGm+oajxH2Hw0H2vl0kpKX32rM3Jl5DibawRwJpcrpzZDdn/55e9SnQd7Zv3ExcUgIhEWFu6T8+NQlgQHz3j8X5EPoap8+YxkfvLeXeOx6gob5hwOMQEHAFdpsNS7MZAD9V/xZWDYlwivsm/cAOygm0x2a1OaduD4bM/XDn90xlmWDNc6Lz9NNPM23aNMrLy7n++uuZNWsWADfddBN79+4FYM6cOURHR3P22Wdz2WWXcccddxAT4xkPt+7EqNejUCpQenu1LXMWuMvvld9+K7GJzueCwtzstmV+qgAAJp40ljhN97I6pVIpDz5+PwuefJgtf2/l2rn/oqhAEHv6g7xMZ40EdxbTBWf2blxSAgXZecffWEBA4LjUm2qx2+1HDeJWlhbhsNuJiO2buK8KCsbHz5/i/Fx2bt9FVWU1UpkMo07f+zbVKl5f8gp2h4M7b7oHk6m2TzEKHEablUtsogaJ1DNmwcXEazDU6Kg1mtwdikAPGBBxv7Ob+yeeeIJly5Yxc+ZMli1bxqJFi9r26e06AagoLcNqsWC2Wo8S98uLCqg1GkhOH9PrtmMSNJibzFSWdv+hPSQyCn91ENn7dve6X4HeY6jRk7M/kwmnuidrHyA2Md5ZVDdr4MV9AJlMyvwbruShx//DX79t4LorbqSsB//DAgKeQq2pFqlUhFgsQaH0Ov4OfcBfpcbhcFBXK9zYuRuTwYDDbm/ny+qJtA4iC0V1BRYsWMBff/3FgQMH2LRpE6tXrwZgyZIljBw5EnDWzlq0aBHr169n/fr1XH755e4M2WNwZnMePYCrr66kqaGeSE3vPXnVIaH4+PkfVVS3vOWzOv2s03vc3qVXXszbH71BTY2O+Zdex7Z/tvc6NoGOycvMJjQyHL8Af3eHQlxyIkX5WqzW7tVeExAQ6JyOZmiVFxUC9DlzXyQSERWfSEl+Lj+vWofSS0lgSFBbn70lThPLS28spqiomP/c+SAWi3At6CsOh4OC7DyPsORpRSiqOzgZEM/9BQsWsGDBgnbLExMTWbFiRYf79HadgNOSB6DJYiXliCyPrL27EIlEJI0Y2eu2YxI1ABTlaQmP7p4nqkgkImXkaHb/sxGrxdLrWQMCvSNj0z84HA63ivsKpZJoTSz5h7KPv3E/IRKJuOLqy4jVxPDAXQ8z/5JreeWtFxk1tvefBwGBgcakNyCVilF4efe7n7lYIgHENDUI9iruRl/tLKKsDvF8W57WzP2q8go3RyIgMHgx6PTtLHlKtM6M6b5k7otEImKTUijMOXw/tqVFkE/tpbAw8eQJfPr1R9x9y33cdv2/eejxB7j0yot7HaPA0eRmZpE8Is3dYQCgSUnEarFSoi0kLsn9hR8FBAYzhpYs+iMHcssKtXj5+BKg7vv9XpQmgaw9O/lzRwanT5+Gt9WCqY/iPsCESeN5/OlHefzBRTz7xHM8/vSjQo2lPlBVVkFDfT1xHiTuxyQcFvfTxghayWDBbbY8Av1H6wibKjgI1REPBtl7dxEVn4iPX+8zP6I1sYhEol747o/G0tyMNutgr/sW6B3bN2wmNDKibQTWXcSnJpGflYvD4XBrHKecOpmlKz7Cy8uLG+ffypoffnZrPAICPcGoMyCVift0He8JYokUS3PTgPQl0Dmt9UrUg8Bz39vXBy8fb6rKBHFfQKC3mHQG/Dvw25fJ5YSE97zg8JHEJCZj0tdg1NVQUV5JxvZdANT1wWIhNi6GT1Z8yKQpk3j68f+y+MkXhexuF2DU6ampqHK7JU8rmhZBX7DmERDoO6YOMvfLigqI6GMx3Vai4522uBKHlZmzzyYgUNU2oNBXLrh4Njfe9i+++2olSz9Y5pI2T1S0Lc4GnpS5rw4OwtvXR8jcH2QI4v4QpCi/ALtIdJQlj0mvo7y4kJSRY/rUtkKpJDQyvMfiflzyMGRyhWDNM8A01NVzYOceJpw62e0j6vEpSZj0BnRV1W6NAyAhKZ5l33zMyDHpPHLfY7z5yttCoV2BQYFBp0cqFeOnGhh7FpncC7tNEGjcjb7GmbmvGgSe+yKRiJDwMKqFzH0BgV5j1BtQHWPLU6rNJzxG0zKrqvcc9t3P4vsVK2lusVXoq12Dn58vr737Mlf/ax6ff/ol/77xbkyCX2+fyMt0zrBIHObeYrqthEdHolAq0OYIRXUFBPpKmy1Py7XearFQVVrSZ0ueViJiNTiA8NAAppw6mYBAtUsy91u5/e5bOOvcGfzv+df5de1vLmv3RCM/OxeJROL2RMwjEYlExCRohKK6gwxB3B+CFOdpqW9sOkrcbxXVW4vb9oWYBA1FPRzFk8pkJAwbTva+3W7P3D6R2PXPNmxWq1steVqJT3VmHeW50ZrnSFRqFe989AYXzZ3Dkrc+5IG7HqaxodHdYQkIdIlRp0Mqk6AODh6Q/ry8fRGLHcJ1283oq2sQicUEtBS+9HRCwsOoEjz3BQR6hdVqpdZoOipz32a1Ul5cQFQf/PZbCY2KQaH0oiD7EN9+9T2TpkxCoVRi1Pc9o1MikXDfw/fwxLOPsX1rBlfPvZ6SopI+t3uikpuZhVgs9hi7BrFEQmxSAtosQdwX6DmLFy9m+vTppKamkpWVdfwdhjjO2bhSvH19AKgsLcZutxERq3FJ+w7AVGcmIS4CuUJOgFpFnakWq8XikvbFYjFPPb+Q9FEjePDuR/huxUqXtHuiUZCdR5QmBrlC7u5QjiI6Po7i/ALhGXAQIYj7QwxLs4Xy4lIaLZZ24r46OJTgPk7lBae4X1FShrmpZ1YNSemjMel1VJYW9zkGge6xfcNmAgLVJA1PdXcoxCZqEIvFaA95zgOBTC7j8Wce5b5H7uHXdb/zr3k3UyEIUgIejL6mBrFYRGBIyID05xsQgEQixqjTDUh/Ah1jqNGhClT1OWN3oAgOD6WqrEJ4IBAQ6AW1BiNwtA9zZWkxNqu1T377rYjFYqITEsnau4eK8kouveIiAgJVGHWGPrfdyoVzL+C9T97CoDdy4/xbKS4U7v17Q15mNtHxsSi9lO4OpQ1NUgKFOfnCjFeBHnPmmWeyfPlyoqKi3B2KR2DU6QlQq9tm15cXOZMnw2NiXdL+pr82U62rQya2Ybfb275TjHqjS9oHUCqVvP3R65w0eSKLHnma119+S7g29ACHw4E2O5e45CR3h9KO6Pg4GurrPcJ1QaB7COL+EKO8uAS73Y7ZYmsT95vNZvIPHSB55GiXWLPExMfhsNsp0Rb1aL/WWQOCNc/A0Gw2s3tLBuOnTEIsdv9HXa5QEB0f5zGZ+62IRCKuvn4er777MgXaQuZfci379x7ocTt2u53GxiaMBiOVFVUUFRSTk50rTEkXcCmGGucNll+A+jhbugb/QKfHe1mhs1C7rkbPrh3CNXyg0VfrUA0Cv/1WQiLCMDc1USdc/wQEeoyxAx/m0oJ8ACI1fRf3AWISU2ioNRAeHsy06dMICFS7JHP/SMZNHMu7H79JQ0MjN119myDw9xCHw0FeZhYJHmLJ00pcciJNjY1Ulpa5OxSBQcaECROIiIhwdxgeg1F/dG2VsqIClF7eqIJck8CzdvU6mixgs1qoLi9t+05xpTUPgK+fL6+++woXX34hH7z9EY/c+xhms9mlfQxVDDU6THoD8SmeMTvrSI4sqiswOJC6OwAB19L64RPLZERGO7P08zP3Y7NaSU4f45I+YhKdDxZF+doeFXjy9Q8gMi6e7L27mTpztktiEeicfRm7MDc1eYQlTyvxqUns2LQFh8Ph9hoAxzLtjKks/eoD7rrlPm6YdzNnnTsDq9VKs7mZJrOZZnMzZrMZc5P5iGVmzOZmzE1mLF1McYyKjiRtxDCGjRhG2ohUhg1PJSh48Ah1Ap5DrcEAgK9KNSD9tc4QqCwrI23sWB657zG2bt7Ge0vfYsKk8QMSgwAYamoICQ93dxjdJiQ8DICq8kr8BomVkICAp9CaQX9k5n6JNg8fP38C1K65d/AJcLYz8+xTkcmkBKhVlPWDfc6wEam8+/Gb3HLdHdx09W0s+fRtomOjXd7PUKSqrII6U63HifuaFhFKm5VLeLSQgS0wcOzbt69X+2VkZLg4EtdQXlKKj79fW3x5hw7io1KzY8eOPrWbkZGBucnM7+v/ZOqpEwETf//xG1Kl835s+9Zt6Opcl73fynkXzkQkFvHN59+Rm5PHv++/FV8/X5f3cyyeen67Q96BQwA02SzdPo6BOt6mFrvizX9twip1j24zmM9tb+jr8Qri/hCjON+ZXalJTmzL1s7atwuFlxexSd0X4rsiLDIcuULR46K64LTm+WvNSuprTfj4+bskHoGO2b5hM94+PgwfO8rdobQRn5rMn2t+oaaiiuDwUHeH046klCSWff0Rjz24iC1/b0OhkKNQKpAr5CgUCry9vVGrVcgVChRKBQq5c71CIUeuUKBUHN5WoVRQq9Njqq8nN0dL5v5M1h9RbCgkLIS04aktgr9T9A8LD/O4QQ8Bz6KhzoRCDn4BqgHpL6TFyk1XWcHB/Zn8s2kLUqmER+9/nC9/WI7qiIwjgf5DV6UjOX24u8PoNq3X96ryih4lAQgICDitGuDYzP08IuPiXXaP8PffO7HZ7cTHOQcNAwLVZO7e75K2j0UQ+HtH7kGn6OMpxXRbidbEIpFK0WbncfL0ae4OR+AEIj09HYVC0aN9MjIyGD/eM5NRPmoykzZ6JOPHj8dmtbLu03c56fQZfYq39Xh/XrWOZnMzV147n7++W4rEYWXS5JP54rUlhAYF9dt7MmHCBE6aNIHHHljES8+8xhvv/4/YuJh+6Qs8+/x2h8J9WYhEIs6adQ5e3t7H3X6gj/eL4CCwWN3yHg/2c9tTWo/XbDb3eiBTEPeHGMX5BTTbbIxs8Vh32O3k7NtD4vCRSCSuOd1iiYSouBiKe1E9OyV9NH+t/p6c/XsZffIUl8Qj0B6b1caOTVsZM3kCUpnM3eG0EZ/q9JPLz8rxSHEfIDAokDfff7XP7eQfyuGJ2+8jPjWJxW+8gFgspra2jkMHDnHwwCEy92dycH8mG//8u82bUK1WkTo81Znd3yL6R8dEeYStkoBnYG5qRCGXDJi4HxHrvCE36Gr4eMlSfHx8eOXtF7j9hrtY9OgzvPzm88KAVD9jabZQZzKhDgp0dyjd5nDmfoWbIxEQGHwca8vT1NhAdUU5I8ZPckn7VquV779dxeRRUegqStr6qjOZsFqtSKWufzwUBP6ek3coG5lcTlS8a/y3XYVUJiNaE0tBjufU0BIQGGzY7XZMegMBgSoAqspKsFmthMdoXNL+z6vXERIWwviTxpG7cyMl+bltfRlcWF+lI2bOOpvQ8DDuue0+rpl7Pf975yXGjBvdr30OVrTZzhlQ3RH23UF0fBxFLcnDAp6PoBgNMbQ5uTQ2Hy6mW1KQT32tiRQXWfK0Ep2g6VXmflh0LH4BKrL37XJpPAJHc2jvfupMJiZM9RxLHoCYeA0SicTjfPddjbmpibeefhGxRELOgUP8vf4PAPz8fJkwaTxXXz+PZ158km9/+opNu/5k6Vcf8vATD3D6jNMw6A18+tFnPPh/j3DBjIuZNn46N1x1C9mHctx7UC4mPz+fyy+/nJkzZ3L55Zej1WrbbfP6668zefJk5syZw5w5c1i0aNHAB+pB2O12bJZmxBIpMnnPMpd6izo4GJvNjq6qil9++pVLr7yYkyZP5K777uD3X/7g68+/HZA4TmQMNc5ixurgwSPue/v64OPnS7VQoFxAoMcY9XoUSiVKby8AygoLwOEgMi7BJe3/9ftGqiqqSBg2nPKiQprN5n7zYj6SVoFf8ODvHrkHs9CkJPbLYEtf0aQkos3KFYqmCwj0knpTrbPIbcu1t6ylmG5ETFyf2zaZatn059/MPO8sZwH1+ESqy8uw22x4+Xi7vL5KR4wdP5qlKz7CPyCAm6++nbWr1/V7n4MRZzFd13y39wcxCRpKtYXYrDZ3hyLQDQRxfwhhbmqipqKKJouV1OFOcT977y5EYjGJI0a6tK+YhDiMekNbdlF3EYlEJKWPJu/gPmxWq0tjEjjM9o2bkclkjDrJs6YyyRVyohM05A8xofpYPn/nI8qKirn32cdITEvhi3c/prGhocNtvbyUjBo7ksuvmsvCZxfwxcpl/L3zTz7//lMWPruA8+aci0Qipr6+foCPon9ZuHAh8+bNY+3atcybN4/HH3+8w+0uvPBCVq5cycqVK1m4cOEAR+lZ1JlqkUpFKJReA9anWCzG4RChq65CIpFw1XVXAjD/+nmccurJvPjsK+RkDe3Ps7vRt4j7g6mgLkBweBhVZeXuDkNAYNBh1B3O5gSnJQ9AZJzGJe1/88W3hIaFMvn007HbbZRoc9v8/Xt6X99TBIG/e9isNgqyc0lI9Uxbs7ikBGqNJnRVNe4ORWAQ8fTTTzNt2jTKy8u5/vrrmTVrlrtDchvHztAqLypAofRCHdz3Yrq///IHFouFmbPOBiAq3lkno0SbR4BahamfM/dbiY2LYelXHzBi1HAevPtRPnz3Y2FA8AhqjSZqKqra6ph4ItHxcVgsFiqEAuqDAkHcH0KUttwgW+12EpKcRW+z9+0mJiEZL28fl/YVm9BSVLcX2fsp6aNpNpspyDnk0pgEnDgcDjI2/kP6xLFtWV+eRHxKItqsnCH75b5z8zbWf7+acy+7kJETxnL1nbdgqNHxw7Kvut2GXCEnbcQwLpo7h0eeeJD3lr49pKYz1tTUcODAAWbPdhbWnj17NgcOHECn07k5Ms/GqNMjlUnw8vEb2I5FEmyWZmZfeB6hYc6HDrFYzFPPP4GPrw8P3v0oTU1NAxvTCYSh2imeDKbMfYDQiDCqhMx9AYEeY9Trj/HbzycwJAwvn74XJiwpKuHvDf9w0WVziEtOBZGIwpystsGEVr///kQQ+I9PSUEh5iYziWme5bffiibZKUYJ1jwCPWHBggX89ddfHDhwgE2bNrF69Wp3h+Q2DK21VVoGVsuLCgiLjkXkAivWtavXERUdSfooZ62myNh4EInaBnINA3Cdb0WlVvHOx29wzqyzee3FN3lywTNYLEKCJ0BBtnPgXpPkueJ+TIJzJklxLzQ/gYFHEPeHEMX5zulcIZHhKBQKDDXVVJYWkzJyjMv7OvxB77nvviY1DalMRva+3a4OSwDQZuVSU1HFhFM9y5KnlfjUZOpMtUPSi9moN7Dk+VeJSdBw2Y3XApA0PJWpM6fz04rvKS8udXOEnkFZWRlhYWFIJBIAJBIJoaGhlJW1zwpYvXo1559/Pv/617/YuXPnQIfqURh1BqRSMb4BAQPab3OzDblczLU3zj9qeVBwEE+/8AS52Xm8/Fzf61QIdIy+zZZnsGXuh1JdXjlkB3IFBPoLk86A/5HivjaPSE28S9r+5qvvEYlEXDx3Dkovb8KiYijKzR6wzP1WBIG/a3IPZgF4bEHy2ERncWdtliDuCwj0BtMRmft2m42KkiIiYvtuyVNrqmXL39uYOfvstppYCi8vQiOiKM7PdWbuD9B1vhWFQsGzLz/FDbddz3dfreSum++hrrZuQGPwRLTZzpnPnmzLExUXg0gkoii/55qfwMDjeSZ+Ar2mOL8Ah8NB8vBhgNOSB+gXcT8gUI2/KoCifG2P95XJFWhS0sjeu4uzL7lSKMboYrZv2IxILGbc5JPcHUqHtE4x1h7KITQi3M3RuA6Hw8H7L7xGY109D7/0NDL54ULGl998Hdv/2szyt97nvmc7tp8RaM8VV1zBrbfeikwmY9OmTdx+++2sWbMGtVrd7TZ6W20enFXrPYkDGbuRSsVYbA6Xx9ZZe40NjRgMdUSE+1Ktq6ZGf/QUfIW3nLNnzeCr5V8TEh7MuIljXBpXf+Fp57YrDu7bj0Qi4VB2Vq+/L91xvI3NZprNZjb8+Rc+fn3POO4ug+ncuoIT7XhPBAw6A6mjRgBgMuipNRqIjOu7uG+12lj59Q+cevoUwiKcRa9jE5PZtXkDvv7OGWEDkbnfilBkt3PyDmXj7etDWFSku0PpEKW3F+HRUW2ZpwICAj2jzZYnUE11eRlWi4UIFxTTzdiyE5vNxjktljytRGkSOLhzO8FRIzBk7OpzPz1FLBZz5723ExMTzdOPP8v1V97E60teIXwIaQE9RZuVS3BYKH4B/u4OpVPkCgVhURFtScQCno0g7g8h8rNyabJaGTY8FYCsfbsJCgsnMDSsX/qLTojrlS0PQPLIMeTs30NNRRnB4Z554zpY2b5xM8NGjcBPNbDZvd0lOj4OiVRK3qEcTjp9qrvDcRm/r1rLzr+3Mv+Om4hJ0By1Th0UyIXXXM4X737Mnq0ZHlcLYaCJiIigoqICm82GRCLBZrNRWVlJRETEUduFhBz2nZwyZQoRERFkZ2dz0kndH7hKT09Hoeh58dmMjAzGj/es81R8MBuRWETqiBEuja2rY/3k/U9paDQjEvmRmpyEX4Cq3TajRo6iuKCEZR98zgVzZreJRp6KJ57brtj68x8EhgQzYcKEXu3vruMVm2388f1PRIaGk9RyX9LfDLZz21daj9dsNvdpIFPAc7BardSZTG2Z+4f99vue2bdr+y5qqnVccsXFbctiElPY9uev6KvKUXp7YRwgL+ZWBIG/Y/IOZpGYluLRCVBxyQlk7z/o7jAEBAYlTqtNKd6+PuTs3wVAuAuK6W75exsJifEkpyYdtTwqPpGdf/+Fl5echrp6LM2WoxLRBooL515AeGQY9//7Qa6+9Hpee+8V0kYMG/A4PAFtTp5HZ+23EpOgETL3BwmCLc8QojBXi9liJTUtBXNjIwXZmSSnj+m3/mLiNRTnF2K39bx6dvKIUQBk7RWseVxJWVEJJdpCj7XkAZDJZcQmaMg/lO3uUFxGWVEJy99cQvqEsZx9yfkdbjPzkjmERUWw7I0lWE/wYtJBQUGkpaWxatUqAFatWkVaWhqBgUd7ildUHLZuOnjwICUlJcTHu8aaYDCir3L6lwf104DtsTSbm1n28ef4+DsHCitLO7aVksll/PeVp2lubuaR+x/H1ovvBIHO0dfUoBpkfvvgtOUBqB6CFmwCAv1FrcEIHPZhLtXmI5ZICI+O7XPbf/66gYjIcKZMO3yPGJvk9HQvzMkmQK0aMFueIxEseo6m2WymKE9LQqpn+u23oklJpKaiilqjyd2hCAgMOow6AwFqNSKRiLLCAuQKRZ/v7yvKKsjOzGHmrLPaDQxGtxTVdTiaATAZDH3qqy+cPGUSH3/5PhKJhH/Nu5m/ft/otljcRWNDA+VFJR5dTLeV6Pg4KkrKaDab3R2KwHFwu7hfXFzMnDlz2n6mT5/elpU5ffp0zjnnnLZ1GzZsaNsvPz+fyy+/nJkzZ3L55Zej1WrddASeQUN9A3VGI00WKynDksk9uA+7zdYvljytxCZqaDabqSwr7/G+/upAwqNjBd99F7N9w2YAxk892c2RdI0mNYn8IVJU12q18tbTLyKTy7nlobsRd1IISSaXMf+OmygtLOaX71YNcJSexxNPPMGyZcuYOXMmy5YtY9GiRQDcdNNN7N27F4CXX36Z2bNnc8EFF7BgwQKef/75o7L5TzRMBqddgp+q+7ZEfWHNDz9TVVHF6AnjAKgsLel02zhNLA8vfICMrTv48J2PByS+EwVDtQ510OAT90PCnQ+plWWCuC8g0F0OWzWoAGcx3bCoGKSyvmVYFhYUcWBvJhfNndNW7wbA1z8AdUgoRblZBKjVA2rLcySCwH8YbXYedrudBA8tpttKaxFIwZpHQKDnGPWHa6uUF7ummO66n3/F4XAwc9ZZ7dYFhYaj9PLG3OT0uh/IorodkZSSxKdff4wmPo67b72PL5evcEm7DoeDhvoGj9cYCnPyAdAkJx1nS/cTEx+Hw26npKDI3aEIHAe32/JER0ezcuXKtr+feeaZo7L+XnvtNVJS2t/cLFy4kHnz5jFnzhxWrlzJ448/ztKlSwckZk+hvLwAb28//P0DKdUWAuDt54dKreLPH3fh5e3TNkrbH0S3WI8U5RUQHh3V4/2T0kezae0qGurq8PYdOD/eocz2DZuJT0kiOCy0y+1Meh0VhfngJvuChNQkfv/xZypLywmLijj+Dh7Md598Tv6hbO564qHjFrwcM3kio04az7cff8YpM04n4IiCeScaiYmJrFjR/kZuyZIlba8XL148kCF5PPUmZ3ZcR9Y4rsZut/Px+0tJTUth7KSJrNXupaai64Hc8y+axeaN//DO60uYcPIExo4f3e9xngjoa3SMnDjO3WH0GKW3F34B/kLmvoBAD2gV1wPUahx2O6WF+Yyc2PfZmN9++T1isZgL585pty42MYWsPTvxV4dT6kZRvb8segx6A3t27WPPzr0Y9AZuvesmgkOCXRBx/5CX6dnFdFtptZPQ5uSSPmGMe4MREBhkGPUG1EGB2O12KooLGXvKtD63uXbVOmI1MWiOsYcFEInFRGoSqKl03pOZBtiCrSNCQoP5YPm7PHzvAv77xPMUFxZz9wN34XA4qDXVUVtbi8lowmSspba2llpjLSaTiVpTbduy4iJn4lGtqY5ak4laUx02m41Jp5zEy28+j4+vj5uPsmO02c5i5JpBYMsTHe+0iyrOLyA+xfMHI05k3C7uH0lzczM//vgjH3zwQZfb1dTUcODAAT766CMAZs+ezVNPPYVOp2tn6zBUKS3L548/v8Xby5dzz7mmzQcrNlGD3W4nZ/9eEkeMQnxEdo6ridbEOqtn52mZOO2UHu+fnD6ajT//SO7BvS55cDnR0VVVk3vwEHNvuLrL7ZoaGlj22gvoqipISIhn2OiBF/g1LV8M+Vk5g1rcz9p7gB+Wr+DUc2Z0q36ASCRi/h038vC//s1XS5Zy0wN3DUCUAkOFpoZ6ZDJnpmV/88f6P9HmFfDfl58mLDIKh8OBoaa6y31EIhGPLnqIPTv38si9C/jyx8/wbynSKNA7mhoaaaxvQD0IbXkAgsPDqCqvdHcYAgKDhlbP+wC1iuqKMpqbmvrst29ptrDymx8ZM34UoWHtZ7/FJCaz+5+NhAV5uS1zv5W+Cvw2m43c7Dz+WP8X33/5I3t27aUg35kAJZFIEIlFZGzdwXufvk1IqGcK/HmZWQSGBHv8jC2/AH+CwkKEzH0BgV5g1OnRJCdSU1GGpbm5z377xYXF7Nuzn0vnXdTpNtHxieQd3I9YLHJ75n4r3j7evPzWC7z4zCt8+uFnrPjsG5qaurZ/kcqk+Pv74x/gh1gsJiIynNjYGPwC/PAP8MdmtfHph8u57fo7eeP9/+HvgQVrtVm5BKhVqDz8Og8QFhWJTCajKE/w3fd0PErc/+233wgLC2PEiBFty+6//34cDgfjx4/n3nvvxd/fn7KyMsLCwtqmlUokEkJDQykrKzshxH2TScemv1fj56emvt7I1u3rKcgtwm53kDZqBMX5OTTW1/WrJQ+AQqkkNDK810V1I2M1+Pj5k71vtyDuu4CMTVsAuvTbt9vtfPfxOxh01fj4q1i9/GMi4xLwHyCbj1Zi4uOQyqTkH8rm5DNOHdC+XUVDfQNvP/sSIeGhXHPXzd3eLzIuhpmXXMBPK75nxpzziE8VRsAFukdzcyNyhQKJtH+/uh0OBx+9t5So6EjOOvdMGuvqsVrt1BoNx93X18+X5155huuvvJGnFjzL868+69EFAT0dfY0OAFVQ17OCPJWQ8FAKe3mPICBwItJmy6NWcWhPBgBRmr7Vmvntl9/R6/Rcd8v8Dte3+u6LJVbqa+vcVmixlZ4I/CajiT279rJ7xx727NrLvt0HqK+vB0CtVjFq7EguuHg2o8aOIn3kcA7uz+SOG/+PG+ffwpJP3+lwsMPd5GVme3zWfiuapES0WbnuDkNAYFBht9sx6Q0EBKooK3IKphGxmj61uXbNLwBMPLnzpL0oTSLgQOklxeSG+iqdIZFIePDx+xkxajgH9h3EP8Aff38//Pz98Pd3CvZ+/r74+fvjH+CPUqloe7bIyMhgfAdOBKPGpPPA3Y9w09W38fZHbxAYNLBax/EoyMlFk5I4KJ6RJFIJkXExFAtFdT0ejxL3v/nmGy655JK2v5cvX05ERATNzc0888wzPPnkk7z44osu62/fvn292i8jI8NlMfQUm81KYdEO7HY7IUEpKORVFBVlUVJeRpPVikwpY8MvaxGJxZjMVpfE2lUbfoEqsg8c6nU/6vAosvbuYtu2rYjFrptl0NzUiPbAHsLjEvAP6tmNuzvPb1/4ffVa1CFBlFVXUl5T1eE2mdv+Jv/APkaccjpB4VFs+uFLlr/5MhNnzhnwL5eg8DD2bN9JygC+3648t2s//47q8kouu+NfHDh4sEf7akYNw2uNN2/99yUu//cN/fbe9/YaJ+B52O12HHYrckX/Z+1nbN3B3t37ePiJB5BKpfj4+2Gz2mmsq+vW/iPHpHP73bfy2otv8v2pJ3NRBzYQAt1DX10DMGgz90Miwtj591bsdnun9UgEBAQOY9TpUSiVKL29KC3IR65UEhQa3qc2V3z+LVHRkQwfmdbhenVwKL7+AVjMDYCz0GJQqHtF744E/sjoSPJy8ti9cy97Wn7yWwYPxWIxyalJnDfnHEaPHYVYJuLc885pd381buJY3vrgtRaB/1aWfPo2YeFdW1kOJHWmWsqLS5l2bnvPbE8kLjmBHX9voamhEaW3l7vDERAYFNSbarHb7QSoVZQXFSCVyQkK69tM9rWrf2HU2JEEdzEjKbJloNg/wMdjMvePZPaF5zH7wvNc0tb0s8/g1Xde4t7bH+CGeTfzzidvesy1vtncTHF+IWMmn+TuULpNdHwcB3bucXcYAsfBY8T9iooKtm3bxvPPP9+2LCLCeZGTy+XMmzeP2267rW15RUUFNpsNiUSCzWajsrKybfvukp6ejkKh6NE+nY0ODgQOh4M/N3yPxdrI9NPnEhYWg8Ph4Lffv8Y23MbeDAXnzTqHHz95G03yME6e3Pds+OMdr3ZPJt8v/YL0ESNQKJU9bt9HCl8vOUhIgD+alGF9CbWNsqICVrz3BiZ9Dbm7t5M+8WROm3UR6uDjP6i48/z2hfraOkryCjj3souYMGFCh9vs2/4P+ft2Mm7q6Zx3xTVkZGRwzmXzWf3ZxzTrqzjlrHMHNOY9Y0ex+de/GDt27ICIPq48t1v+2MiB7bu48JorOP/Szqc/doXl9nqWPP8aZn0tU846wyVxHUlGRgbp6emCwD9EqDPVIpWKUXr1v3fkR+99gjpQzZxLzgecogkiCeamhm63cd1N17Dl760sfupFxowbTXyipp+iHdoYWjL3Pd2eoTOCw8OwWCxt3rICJx75+fk89NBDGAwGVCoVixcvRqPRHLXN66+/zmeffUZoqPPBe9y4cSxcuNAN0bofY0s2J0CpNo/I2Pg+FVnU5mnZviWDO++7vdN7LZFIRExiMvmHnIkKRp37xX04WuC/6pLrsFmt1NU5s/JV6gBGjRnJrAvPY/TYkYwYORxvH++2fTMyMjpNnBg7YQxvffgad/zLKfC//+nbhEWEDcgxHY/8Q9mA5/vttxKXnIjD4aAwT0tKeseDRwICAkdzuHC6mkO7DhAeHdOnZ+Hc7DyyMrN5YMF9XW7n5e1DcHgEFkt5WwxDmSnTTuHND17lrlvu5V9X3sR7S98iKqbndSJdTVG+Frvdjia5/2pjupqYhDg2/fI79bV1+PgJtTI9FY9Jo/ruu+847bTTUKudU2YaGhqora0FnKL2mjVrSEtz3jQEBQWRlpbGqlWrAFi1ahVpaWlD3pJnz95NlJbmMX7cGYSFxQDOG/KRI6Zis9mZMmsMcinUVJSRPHJgChnGJGhwOByUaHtXPTth2AgkUik5+3a7JJ69WzfzycvP4nDYuerO+znlrPPI3JXB2089wrqvP6O+1uSSfjyNnZu3YrPZOrXkKSvUsmr5R8QkpjDz0nlty8dMPpW0sRP448dvKSvUDlC0TuJTk2mor6eytGxA++0ruqpqPnzpDRLTUrjwmit63c6p58wgPjWZL979iKaGRhdGKDAUMer0SKVifPrZb//QwSw2/bWZeddcjvKIAVupTIHN2tztdsRiMU89vwgvpZIH734Es7lr/0yBjtFXt9jyHKdYt6cS0pIlVVUmFNU9UVm4cCHz5s1j7dq1zJs3j8cff7zD7S688EJWrlzJypUrT1hhH8Co1xOgVmG1WKgoKSZS0ze//W++/A6pVNI2WNsZsYkpLXVdxB4l+rQK/KPGjOTc88/hqeefYOUv3/D7ll947b1XuPG265l48oSjhP3uMGbcaN7+6HX0NTpuuOoWykq7Lhg/UORltoj7qYND3G8Vp7RZOW6OREBg8NCaNe+vCqCiuIDwGE2f2lu75hfEYjFnnzfjuNtGaRKRycDkgZn7/cGESeN595O3qK2t4/orb0brAVaRrXVKBpO431pUtyhf695ABLrEo8T9Iy15ampquPrqqzn//POZPXs2+fn5R93sP/HEEyxbtoyZM2eybNkyFi1a5I6wB4yCwkPsP7CFxISRJCeNOWqdrryGvX9kEhQWwLZtvwKQnD6mfSP9QExLNfbeftDlCiVxycPI2rerT3HYbFbWffM5K5cuITIugRsfXEh86nCmz7mU2xc+x6hJU9j256+8ueghNvz8I81DTGjavmEz6uDADjN96kxGvnrvdbx9/bj0xtuP8usWiUScd+W1+Pj7893H7w7o+9JabT3/0OB5ILDb7bz33P+wWizc9uh9SPvgfS4Wi7nmrpvRV+tYufwrF0YpMBTRV+uQSMUEqPvXM/LjJUvx9vHm8vlzj1quUHoDjh5dI0LDQlj03ONkZWbz6gtvuDjSEwN9TQ1KL68eC1eeQkiE006kulwQ909EampqOHDgALNnzwZg9uzZHDhwAJ1O5+bIPBeTzkBAoJry4kLsdhtRcb332zebzfzw7WpOn3E6wSFdF4+NafHd9/KRu72o7rEMG5HK60te4dEnH+L8i2YRp4l1iZ3hqLEjefvjNzEYjNx41S2Ulrg/2SQ3M4uImGi8fft/lp4rCAwJwi/An4IcoaiugEB3afW7F2Gj2WwmPCa21205HA7Wrl7HhEnjjnudB4iKd/rum4yedZ3vT0aOHsH7y97BarXyr3m3kNUyiOoutFk5ePv4EOIhM8a6Q6vmV9xSoF7AM/EYW561a9ce9XdMTAzff/99p9snJiayYsWKfo7KM9DrK/lny88EB0cyYfz0dje0RXlaKrTVmGqsOAKrCI+P65YFjSsIiwxHrlD0uqguQEr6aH5esZyainKCwnruK1pfa+LbD9+hIDuTk06fwZkXXYZEcvhf21+lZva86zh5+tn8/sM3/LnqOzL++o1Tz72AMaecetS2gxFzUxN7tu5g2rkz2k3ps1mtfPPBWzTW13PtvQ/j49e+WryXtw8XXnszn772POu++ZzZ864bkLij4mORyWTkH8ph8pmnDUiffWXtNz+wL2MXN9z/b8Kj+z6tL3lEGlPOOoOfvvqO0887m7CovvktCgxdqsvLEIlE/XptLykqYd2a9cy79nL8A46+Vnj7+lOrN1Jr0PfoOj1t+qlcec3lfPbJF5x8yklMmz44C2i7C321DtUg9dsHCG4pVllVXunmSATcQVlZGWFhYUgkzppKEomE0NBQysrK2s22Xb16NRs3biQkJIQ777yTsWPH9qivwVhHqyOqK6sJjAhly8Y/nX+b6qjrZYz/bNyC0WBk9Pj0tuPs7HgddjtSmQxvbxkH9+3HL3xwzhY6ku6e23seupOXnn2Va+Zez38eu6dLz+r+xOFwkLlnP3Epib36v3TX/7I6LISDu/cNeP+e9tntbwSbzaFD6+yohjoj0LdiuocOZFGQX8g1N3RcMP1YojXObPHmpvpe9zkYSRmWzIefvcst197BjfNv5c0PXmPk6BFuiUWbPXiK6bYSGBKMt4+PUFTXwxncquYJQJO5gb82rEQuV3LqlAs6FKIz9x7AZrcTpNJQ35iLb3QAZnMjCkX/FzYSSyRExcVQlKvtdRtJ6aNhxXJy9u/psbhfWpDP1++/SUNdLRdccyOjTjql022DwyOZe/OdFOXl8NvKFfz05ads+W0dp59/MWljJwyqC+yR7N22k2azmQlT21vyrP36M4pys7nwupuJiInrtI245FROOes8/l63msS0dNLGduzb70qkUimxSfHkD5KpvEV5Wr5872PGTzmZ02fNdFm7V9xyHds3bOaztz7gnmcWuKxdgaFFTZVTHA3uYW2ZnrD0w+WIxCLmXzev3To/lYpafRGmHor7AHc/cCcZW3ew8KEn+WrV54S4STgZjOirawa1V71CqcRfraKqzDMsLwQ8kyuuuIJbb70VmUzGpk2buP3221mzZk2bVWd3GGx1tDrCarXS1NBAYkoyYls9fio1U06d1uv23nzxXWJio7nqmnmIxeLjHm/2to001u/Gx8vbo96X3tCTczt+/HjShqdx63X/5n/PvcGST98mOja6nyNsT01lNQ21dUyccnKP3393/i9nj9/LTyu+Z/SoUUhlsgHp09M+u/2NUEdraGHU6ZHKpOiqypFIpQSH9/7e/ufV65BKJZx5dvfqtwVHRCIWS5CIbTSbm5Er5L3ue7ChSdDw4edLuOWa27nl2tt57d2XmTBpYK8jVquVolwtMy6aNaD99hWRSER0fFyfEnoF+h+PseURaI/dbmPjplU0NtUzbeocvDoppKjNzqXJYiXAR0bloWIc2NmybR0Oh2NA4oxJ1FDUh1E8VVAwIRFRPbbm2f3PJj555b+AiGvvebhLYf9IYhKSuObuh7jslrsQSyR8++HbfPTi02izDvY8eA9g+8bN+Pj5MmxM+lHLMzb8zo6Nf3DKWeeSPuHk47Zz2qw5RMbFs/rzTzDqB2bKfHxqMvlZOdjt9gHpr7c0m5t56+kX8fH15Yb/3OnSgSB1cBBzrr6cjE3/sHf7Tpe1KzC0MOpqAPr0ANAVuho9K7/+gVkXnNthYcHWGQPV5T23LVAoFDz3yjM0Njay4D+Pe/zn3ZMw1OhQD+LMfYCQ8DCqhcz9E5KIiAgqKiqw2WwA2Gw2KisriThmkDIkJARZiyg4ZcoUIiIiyM5277R5d1BrcGZxqgLVlBbkEdkHS57c7Dx2bN/JxZdf1O1CjbGJKcjkYvQ11b3ud7AyPD2N9z55i4aGRm6cfytFBcUDHkPeoSwAEoalDHjffUGTnIDNaqVYK9g1CAh0B6POQIBaTXlRIWFRMb12EWi15Dl5ysmo1Kpu7SMWi/FTB+PlLWuzBzqRiIqO5MPPlxAeHsYdN/wfm/76e0D7LyssxmKxDCq//Vai42Mpzi8YMI1RoOcI4r4Hs2PnH1RWFjFp4tkEBXWcKelwOKipqKTZZsNUXY5MLGf0yKkUF+eQm7d3QOKMSdBg0hv6VIAreeQYinKyaWpoOO62NpuVtSuW8+OyD4hOSOKGBx7r8XQ2kUhEysgx3PzIk8y+6npqjQaWvfYC29b9SEXx4Lk5tVqt7Px7K2MnTzzK/70wJ4u1Kz4jcfhITj//ki5aOIxEIuXC627GZrXyw9L3B0SAi09NoqmhkfLi0n7vqy+s+GApRXlabn7obvxVri9oeu7cCwmNjODT19/DarW6vH2BwU+tobX4Vv947n/x6Zc0NZm59sarO1wfFOYU/KsrepeBnZAUzwOP3c+Wv7fxyZJPex3niYTD4XDa8gQNbnuMkPBQqgTP/ROSoKAg0tLSWLVqFQCrVq0iLS2tnSVPRcXh/4+DBw9SUlJCfHzvhe3BSmuRRaWPEn1VZZ/E/W++/A6pTMqcS2Z3e5+YJGfdplr9iSfug9Pb/72lb9HU1MSN82+hsKBoQPvPO5iFRColNnFw/e/HtYhUBdm5bo5EQGBwYNQb8FcHUF5U0CdLnj279lJWWs45s8/u0X4h4VEolFJqKk/Me7PQsBDeX/4u8Yka/u/W+/h17W8D1re25Tqpaak9OJiISdBQX1uHvrrG3aEIdIIg7nsoubl7ycrexbDU8cTHD+90O6POgLXZgp9KRf6h/SSNGEVa2kTCw2LJ2PE7JlP/Z2DHxGsA+jRNJzl9NHa7jbzMrqcb1pmMLH/9Rbb9+SuTzjibq+64r0Mf+e4iFosZM/lUbn/8v5x54WUYqypYsngR33+yBMMgyFzK3L2P+to6xp962JLHqKvh6/ffRBUczEXX3dLtjC2AwJAwZs6dR0F2JpvX/9QfIR9Fa1FdrQdb8+zL2MVPX33PWRfOYvSk/rErksllXHXHjZQWFLH++9X90ofA4Kaxvg6HA7x9/VzedlNTE18sW8HpM04jIaljUSEoNBSbzY6huvfXxYvmzuGsc2fw5v/eZu/u/b1u50Shoa4eS3PzoM/cDw4Po7qiCntL9rbAicUTTzzBsmXLmDlzJsuWLWPRokUA3HTTTezd60xCefnll5k9ezYXXHABCxYs4PnnnyckZGBqR3kSrVmUNksTAFGahF6109TUxI/frWbG2dMJ7IGtV2RsPCDC3Fjbq36HAqlpKby39G2azc3ccNUtFAygv3BeZjaxCZpBZ5MRFhWB0ssLbZYg7gsIdAej3oC/yhdzUyPhXdjmHo+fV61DLpdz+pk9s2+Linf6vRfkeu7zd38TGKTmvU/fZnh6Gg/83yOsXrlmQPrVZuWiUCqIiI4ckP5cSXS8839V8N33XARx3wOpqi5hW8Z6wsPiGDO664t164crLiESc2MjySNHIxKJOPnkc5FKpGzavLptOnR/EZPQ8kHP6/0HPUqTgLevL1l7d3e6TWlBPh88/ySlBfnMufYmzrrkCsQtRdr6ikwuZ/KMczjt0vmcMuMcMndt5+2nHmHdN5/TUOe5DzkZGzYjVygYNXEcAJZmMyuWvIHNauWym+9C6e3d4zZHnzyV4eMm8ueq7yktyHd1yEcRFReLTC4n75BnTr+vM9Xy7n9fITI2mituvb5f+xp3ykmMnDiWbz/6DFPL1HwBgVaamxoRiSU9GqzrLn/9uhGT0cT1N1/T6TYBahVWix2TofcDxiKRiMeefoSQsFAevudR6mrret3WiUBrZow6eJBn7keEYbNa0dfo3R2KgBtITExkxYoVrF27lhUrVpCQ4BSslyxZwsiRIwFYvHgxq1at4ocffuCbb77htNNOc2fIbsOoMwBQb9KDSEREjKZX7axbs55aUy2XXHlxj/aTymQolL6ApVf9DhVShiWzZNnbWC1Wbpx/K/l9qCvWXex2O3mHsklIG1yWPOBMlIpNikebk+fuUAQEBgVGnR6l0mlF11VNvK6w2Wz88tN6Tj19Cr5+vj3aN2FYGgAVRdpe9T1U8Pf3452P3mD8SWNZ8J8n+Przb/u9T212LrGJ8S7TsAaSVnG/qA+an0D/Ioj7HkZDQy0bNv6At7c/U06ZdVwhJ2u/0yc+LDQAiVRKwjBn1W9vL18mTZqJXl/Jnr0b+zXmgEA1/qoACvN6LwSLxWISh48id/+eDu1gdm3ewCev/BexWMx19z7CyInti8e6AplCyfQ5c7n98f8ycuJktv2xnjeeeJC1X39GTUXPvab7E7vdzvaN/zBy4lgUSiUOh4NVyz+mvLiQC6+9qdfe3CKRiHOvuAbfgAC++/hdms1NLo78MBKphLikeI/M9nE4HHz40huYDEZuW3A/CqWyX/sTiUTM//fNmJuaWPH+0n7tS2DwYbM2I5W5PpvP0mxh3epfGT9xLKPHjup0uwC1CqvVTkNt3wY7/f39ePalpygtKWPBfxZSX1ffp/aGMm3i/iAuqAtOWx6AasGaR0CgS1rtLfXVFQSHRaDw8upVO19/8S2ahDgmnDSux/v6BwYjl4upM5l61fdQISkliSXL3sZms3PT1beSl9O/yS7lxaU01jeQMCy5X/vpLzTJiRTm5AsztAQEjoPdbsekNyCW2JFIpYRERPWqnYytO6iuqmFmDy15AEIjo2g2W9FV9s5qcyjh7ePN60v+x6mnT+Hpx//L0g+W9Vtfdrudgpy8Qem3D+AX4I8qKFDI3PdgBHHfg7DZrPy18QesVgvTTp2DQnH8m/rMPQew2uw4muvQpKQhVxwWIKOjkkhKGs3BzO2Ul/fvhzAmQdPnUbzk9NE0NtRTkn9Y6LVZrfz05aesWv4RMYnJ3PDA432avtZd/NWBzL7qem5+5EmSR4wmY8PvvP3Uoyx//UUO7d7hETev+Yey0VfXMKHFkmfz+p/Zn7GF02dfRPLIMX1q28vbhznX3oShuoq1Kz5zQbSdE5+ajDYr1+OKbG5c9xtb/9zEpf+a32Yf1N9ExcVw1sWz+WP1OvI92KpIYGBxfjbsKJQ9n4lzPH5atRa9Ts91N1/b5XY+/n5YrXbMTcevi3I8xo4fzX0P382fv23gsvPnkbF1R5/bHIroq52zJAZ75n5wuLNeg+C7LyDQNUadHoVSSXlRQa/99rMP5bBn514uufwiRCJRj/cPiYhBJBKRvX9g6nZ5MknJiby/7B0Abrr6VnKz+y8zPS/TWUw3cZAV021Fk5yIuanJ42toCQi4m3pTLXa7HZulidCIKCTS3hXT/Xn1Ory8vTj19Kk93lcml2GxQH2tXiiOCigUCl568wXOPm8GLz/3Ku+8vqRf3pfK0jKaGhrb6pQMRqLj4yjK17o7DIFOEMR9D8HhcLBl2y/odOVMPvlcVAHB3dqvpKAQpNBYZyKlA0F33JjT8PcPZPM/P9Fk7rso0xkxCRpKtIV9Er0T0kYgFkvI3rcLcPrrf/ra82Rs+J2TzzyHebff2y9+010REhHFRdffwl1Pvchpsy+iuqKMFUve4I0nHmTj2lXU17ovs2n7hs2IxWLGTj6J3AN7+e2Hr0kbO4EpZ89ySftxSamccvYsdv+zkQM7trmkzY6IT02mqbGR8qKSfuujp1SWlfPJq+8wbHQ6sy6/aED7vuiaK/EL8OfT194TbrgEAKc9lFQq7lN9kY6w2+18vGQp0bFRTD3tlC63FYvFSKRyrBazSwbirrruSj787D1EIhE3zr+Vl597FbPZ3Od2hxL6Gqe4rwrqnyLKA0VwmDNzXxD3BQS6xqg3oA4OoKGuttd++19/8S1yuZzzL+rdvWBUQiIOhwPtoYO92n+okZAUz/vL3kEsFnPj/FvJ6afEi9yDWSiUSiJjo/ul/f4mLtn5/ypY8wgIdE1r4fSGOiPhvSyma2m28Ova3zn9zGl4efVuZrlYosBusw6KGoMDgUwm5b8vP82cS87nndfe45XFr7r8ObzVqUCTMnjF/ZiEOEq0RR6R6CrQHkHc9xAOZe1Aqz3AyPRTiInu3pRMh8OBSadHHej0WUtKH91uG6lUxpTJszA3N7F167p+EwtjEuJoNpupLOv99C6llzexySlk79tDiTaXDxYvoryokAuvu5kZF13mVm8yX/8ATj3nfO5c9DyX3nQHgaFh/PHjt7z22P18/8l7FOflDLgQu33DP6SNGYm5sZ5vP3qHsMhozp9/Q68ytTpj2nkXEKVJYM3nn2DU9U9l9PiWL7i8QwOfqW61WCgtLGbn31v5acX3fPTKWzx33wIW3novYpGYWx++d8D/73z8fJl74zVk7TvA5t/+GtC+BTwTXWUVEqkYvwCVS9v96/cN5OXkc875Z3fruiFvmU3mqkHNsRPG8NWPn3HpFRez9INlzLvoGjL3H3JJ20MBQ3UNPn6+yBUKd4fSJ+QKOaqgQKrLK90dioCAR2PU6/FX+QD0KnN/wx+b+OHbVcw4ZzoqtapXMQSHhWFuslJWqO3V/kMRTYKG95e9g1Qm5ab5t5GV6fo6UXmZ2cSnJg1KH2aAKE0sUpmUgmzPs9kUEPAkjHoDMpkYq6W5124EmzdtwWgwcs6snlvytOLlGwBAiVb4zLYikUhY+OwCrrz6cpZ+sJxnFy52qbOANjsPiVRKtCbWZW0ONNHxcViam/uk+Qn0H72bByTgUsrKtezc9Scx0cmkjzi52/vVVFbhsNsJDvYlLDqWAHXHvrxqdSijR01l564/ycndQ3JS+0GAvhKdoAFgw8+/MWxMOl7e3nj5eOPl7YWXjzdKL69uiUfJ6aP55Zsv+OSV5/BTqbn+vkcIi/acC6BYImHY6PEMGz2e6vIyMjb8xp4tf7Nv2z+ER8cyftp00idMQibvXzGmpKCIsqJipl8wk6/eex2JRMLcm+90uQgkkUiZc+3NvP/cQlYuXcL8ux5weUHPyNgYFEoF+YeymXr2GS5tG8BmtVFVXkH+wWyqtSWUF5dSXlxKRUkpVeWVOI740vb29SEiJoqRE8dxxuyZBLd4RQ80p507g19/WMPnb3/IuFMmoexlVobA0KCi1DmrJSCoezO6ustH7y0lIiqCiZMndGt7bx8/rM1mag16lw00ePt48+iTD3Hamaey6JGnmX/ptdzy75u4/pZrkfZyqvJQQV+jG/R++62EhIdSVSZk7gsIdIVRZyAwSIHdJiU0svsZ3JZmC6+//CZLP1hOyrBk/n3v7b2OIUCtoqHegqG6ApvV2mvLiKFGXHwc7y97h5vm38bN19zGu5+8RaqLit9aLRYKcnI5++ILXNKeO5BKpcTEazyyhpaAgCdh0htQevW+mO7Pq9bxzMLnCAoOZPLU7utGxxKgDsZQpaNEm0f6hN63M9QQi8U88Nh9ePl48eE7H7Pxr78Zf9I4xk8cy7iJY4mNi+l1IqU2O5fo+DikMpmLox44YuI1gLOobnh07+pFCPQfwh2bm6mtNbDp79X4+wdx8qRzenSxyM/KQSIRoZDRoSXPkQxLHU9ZuZYdO/8gNCSagADXevhGa2JRenuxctmXrFz2Zbv1IpGoneDv5eNz9N/e3ojFDkRiMYGhEZx54RX4qTzXazg4PIKZc6/ijAsuYe/WzWz/6zdWf/Yxv373FaNPnsr4aWcQGBLWL31v37AZgOqyHHRVFVz17/tRuVj4ayUwJJSZc+fz47IP+PuXNUydOdul7UukEmKTEvhj9Vr2bt+JTC5HJpM6f8vlyOSylmWyw6+PXH7M7/raOipaBPzy4hKqyiqwHTF1TOntRXh0JAmpyUw+8zTCoyOdP1GR+Ab4u3TmQ28RSyRcc+ctPHnnA/y4fAVzb7za3SEJuJGaCqcoGhwW7rI2d27fxe4de3jwsfuRSruXKegboMJQVU2tQQ+99IPujKmnTeHr1V/w30Uv8Ob/3uGv3zfy1PML0bQMHJ+I6Kt1qEM89zuwJ4REhJG9P9PdYQgIeDRGnYGg4GAiYuK6LaoXFxbz4D2Psn/PAS676lLufej/UCp7nxDgr1LR2GDBbrdRrM0lLim1120NNeI0sXyw/F1uuvpWrrrkWkJCglGpVagDVajUzh916+9ANSp1AKpA57IAVUCnA9ZFeQVYLVYSXTRY4C7ikhPYvmEzDofDI+6lBQQ8EaPegMJLilgs7tEgbm1tHc8tep7VK39i1JiRPPPik8gV8l7HoQoKpKLISva+3YybclqvC/sORUQiEXfddweJSQn8/ssfbPrzb1Z9txqA4JAgxrUI/eMnjiMxOaFbiY8OhwNtVi7j+zAg4wlEaZyDG8X5BUyc1rWlq8DAI4j7A0hJUQnr1/7Gr+t+p9ncTGpaEimj1EikMGbkGchkPbtA79m+Cx8/Z6Z2cgeWPEciEomYPOkc1vy8lL83r+bss+Yhkbju9CuUSv73xYfoq2torG+gob6exvoGGhsaW363LmukqaGBxvoG6mtrqSqrZuEedAAATG5JREFUoLHlb3NTEwASqRjbnnI2/uIssqj09iIoNITAkCACQ0IICg0mKDSYwNAQAkOCCQoJRul9/OLD/YVcoWT8qWcwburpFOZmkfHXb2z781e2/L6OhLR0JkybTtKIUS7NeN++YTPJw2MoyDrIOXOvQpMyrFv7ORwOanTl5OTsobS0AKuthqCgcIKCIgjwD+o0xlGTTiH3wF7+XP098alpRGlc6xV38bVXsmHtb1gtFizNzViaLVgsFupMJufrI5Y5XzdjtVg7bU+hVBAWFUFMgoaJ004hPDoSY30dp515Bv5q1aB46EgZOZxTZpzOmi+/5bTzziI00nXCrsDgQl9dBUBYlOtuvD967xNU6gAuvPQCDhw80K19VEHBGKpyMBn0LovjSAJUATz3ytOcMWMazz7xPFfMmc/dD9zFZVdd6vIZQ4MBfXUNkXEx7g7DJQSHh7Hl943YrDYk3RxMEhA4kbBarS33PN7dtuRZu3odTy14FpFYzEtvLObMmdP7HIdcIceBFJFIzOdvvsz4qWcw+axz8fUP6HPbQ4GYuGg+/HwJXy3/mprqGvQ6Awa9gUJtEQa9gbq6+k739Q/wbxkECGgbBPAP8ENXWgbAtu27yMzJdyaztP7IpUiP/FsmPWLd4b+rK6uprqpGLpcjV8iRy+UD/r0Zl5zIH6vXUVNZ1VZrRUBA4GiMOj3e3nJCIqO6ncG9Y9tOHv3PQirLK7n1rpu58bbr+zy7NSBQRXVlHf7qWt579nFGnzyVaefNwb8TJ4gTkVlzzmXWnHOdwnxeARnbdrBj204ytu5g3Zr1gPPZZeyEMW2Z/alpKR2eG11VNXUmE5rk3tXT8RQUSiWhkeEU5WndHYpABwjifg/Y+Ot6tm/ZiPbgPiKjY9AkphAeHdWlUFigLWT9z7/y69rfObDPWZxqeHoa6kA1Ct8GHA4fPvvgT557dBkhYSGkDEsmdVgyKcNSSE5NIi4+ttOLd87BLHx8ZXj5+HVrWpeXly+TTprJXxu+Z/eejYwbe3pv3oZO8fX3w9e/9wVvbVYbTY2NNNTVo6+uQVdVTU1lNTVVVegqna8Lc7UYde2FJW9fH+cAQGgwgSHBBIYEERQWTPLw4YRHRw6ImCsSiYhLSiUuKZVao4Gdf//Fzo1/8NW7rxEQFMyYk6cSHB5JQGAQ/upAfHz9EPXixrumsorqskKiYgMYc8o0xk87/sOcxdKMtuAgObl70OsrkUplyGTeFBZlkZu3F3Ba8AQGhhEU6BT7gwLD8fFxZrKLRCLOu+IaivNz+e7j97jpoSdQKF03oDJy4jhGThzXo33sdnvLYIClbQCgubkZL28v1MFB7c55RkYGAYHHL0zpcDiwWpqRSGVuFxSvuOU6Mjb+w2dvf8DdTz3q1lgE3Edti5geEhHhkvZysnL46/eN3HbXzXj1YGA0MDSE/IMOdJX9a68yc9bZjJ0wlkWPPs1zT77A7+v/YNFzjxMeceIMcNntdgw1OtTBQ+MhKyQ8FJvNhr66xm12ZwICnoxJb0ChlOKw24mM6/rhv7GxiReeeYlvv/yeUWNH8t+XnyYqOtJlsfgFqPEJiCAhNZqtf/xCxsbfBZH/CCKjIrj7gTs7XNdsbsZgMGLQO0V/vd6AXqfHoG9ZpjOg1+spKy3n4P5DmEwmgr0U+MrlvP/uJy6NUyqVIFcokLfMbFXInaK/TC5DoVC0/G5dJkehkBMYpGbY8GEMTx9GrCa2R/fB8cnOxJ+C7DxB3BcQ6ARDjR6ll7RbfvuWZgtvv/4eH737CdExUXz0+RJGjR3pkjgC1Crqapu54rb7ydy1he1//ca+7f9w0ulnccpZ56H09nZJP0MBkUhEfKKG+EQNl15xMQ6Hg9KSMnZs2+kU+7ft5I/1fwJOu9Ex40YxbqLTymfEyOHIFfIOi+laLFbqauucP3XO37WmWmpbl7UsrzU5X9fXN2AymQgMDEQqkyKRSJBKpUhlUqRHvJYc81omlSKVSpFIncsVSgWjxowkKSWxVxpZdHwcxfkFrnlzBVyKIO73AENtGaEJfpipJr+8mvzynViardgtDqQSBb4+/gQGhSKRKTl0IJcNf/zD3p0HsdnsjBozknsevIsZM6cTFRPFnr1/s2//ZlJTJvLwY9PIyswiKzOb7EM5bNm0BavVaSOiUChITIonMSmBiIhQ1KoAvOQyag1GyrQFxCcGkDp6bLdF4uioRJKTRpN5KIOIcA0REZp+fMd6hkQqwcfPFx8/X0IiwnA4HNhsVpqbm2hubsLc8ruxsR6jXo/JaKC+zkRTUwPNzU3YbBbs2BFL6mmQNdGoLyX3l200mppRKnwJC48mdfhI4pNSXTproSP8AlRMO/cCppx9Hll7drH9r9/4c/X3xxyvFD+VGn9VIAGBgfirAvFXBzrFf1Ug/oGBKL3af7Fu+GktEdH+hEbGcM7cq7q8KOv1lWTn7EZbcBCr1YJKFcLE8Wei0aSxZ88+xo0bR22dgZqacmp0ZdTUlJOVvQv7oQwAFAqvNqE/KDCcWVddwxdv/o+1Kz7jgqtvcOl71lPEYrHzoaWTOgM2m5WmhgaaGuppbGigskjLXnszTfX1NDY20FRfR2Pb+nqaGuqds0oa6rFZrUhlckIjowiNiiEsMprQ6BjCImMG9GYnMCSYC+Zfxor3l3Jw117Sxrjmhk5gcFFfa8LhcODl4+uS9j5e8ilKLyWXz5/bo/1UgWqsFju6lpkE/UloWAhvLPkf3371PS8++wqXzrqShx7/D7PmnDsoZt70lVqDEbvdPoQ8950DM1XlFYK4LyDQAUa9AaWX8940UtN55n5Odi4P/t8j5OXk869br+O2u25BJnPtPW1AoIpaUwMXXH0DU2fOZuPaVYLI303kCjmhYSGEhoV0e5+Hrrud4PBQPn3yUecM1bYfa8uM1cN/W1vWNTc3H7U+OzubyIhImlsSXZqbnds0m51/W5otmM1m5/KW35bmZkzG2qP2qa6sprm5GQAfHx+GDU9h2Ain2J82Io24+FgknRT9jUnUIBKL0WYPfusJAYH+wqirQSwWERGj6XK7/Fwtj97/OAf2HeSiuXO4/5F78PH1cVkcrQlvzU3NnH3JlUw8bQZ/rv6Ov39Zw85NfzL1nPMZf+oZg9ofvr8QiURERUcSFR3J+RfNAqCqspod23e2Cf5vvPwW4NTyRowcDo0NADxwz2PU1ddTW1tHU2PTcfvy8vbC188XP19ffHx9aGxopMZeg9VqxWqxYrXZ2l7bbFasVtsRr61tmmJHhIWHcsq0yUyddgqTTjkJX7/uPWdGx8ex8++tNJub+2QNJeB6BHG/B8yaM58tW/4mOiqc4kItleWlGOt1NJvrMTtM2BxNNNoMAHgFwNlzRnL2nJGIkOCl9EEhd7B753q2/N1Ik6Meb6kfdlMz/n5KJoxLY+SweOrrTkVXpSM/J4+SgmL01dU0GQ3kb88g/4hYHA5QeEuRSMSkHsdv/1jGjjmNyspiNm/5mfPOuQalcuBHZh0OB2ZzI/X1JurrjdS1/K6vN1HfUIvZ3EhzcxN2e+cXJJFIhFyuxMtfSYA8ALlcgVyuRC5XIhXLaGpopKy0CInYgFjmwGguYevOEv7JWIPDIsLXR0VUdDwxcYmo1aH98j5IJFLSxk4gbewEGurqMOlrMOl1GPW6lt/Ovwuys6g16o8q7gogVyoJUAfhr1a3CP5B7Nv2ByDiyjvu6fAL12q1UFB4iJyc3dToypFIpMTGpJKcNIqgoIijhDGRSIS/nxp/PzXxmjQAbDYbRmM1NbpyamrKqNGVU1qa17ZP0tRRlFcW8Mf67xgx+iTU6lCk0oH94m82mzHqqjHqajDqajDUVGPUVWOocU55a2qsp7mp/RdmxhGv5UolXt4+KL198PL2ITg8su1vhZcXDbW1VJYWc2j3Dnb9/Vfbfv7qIMKiYgiLjiY0MoawqBjUIaH9luV/7twLyT+U3S9tCwwOzI0NgNglonZZaTk/r1rLZVfNRaVW9Whff7UKq9WOSa/rcxzdQSQSccnlFzFp8kQee3ARC/6zkN/W/c6Cpx4hMOj4s3AGM/oa53usGiKZ+62CflV5JWlujkVAwBMx6Q14ecmQK5Sog9sPgDkcDr796nteePolvH18eOvD15k8dVK/xBIQqKYg25llGBga1oHI/wfjTz2dyTMEkb+vNDY0UFJQxEmnT22pHdXz+2mr1UJwhopJkyb3OR6LxUpeTh4H92c6f/Zl8vXn32I2mwGn0DRseCrDhqcyPD2NtPRhxCdokEgkKJRKImKi0GYLRXUFPAOrxULO3oMkxGlQB3tGDaO6Wj0KGUTEdpy573A4+Przb3npv6+g9FLy8pvPM/3sM1weR4DaeR9t1BsAUAeHcOG1N3Py9Jn8tvJrfvn2C7b+8Qunz76Y9AmTOk0kdTgclBWVtMw6632x2cFOSGgwM887i5nnnQWAQW9gZ8Zudmzbwe4de7HXNyCSyUgaloyvnw9+fr74+vo6hXt/P3z9fNtEfF8/H/z8/fDx9Wnn4JGRkcH48eO7HZczWfbIAQAbdbV1bP1nO5v+/Jtf1qznu69WIpVKGD12FFNOO4Up004hZVhyp+cyJkGD3W6nrLCYuEFuMzTU8Ahxf/r06cjlchQtGbj3338/p556Kvn5+Tz00EMYDAZUKhWLFy9Go9EAdLmuvxCJRMhkSqJjEomKTiDzwCF++flX1v+cQaG2kABfL0alJxIVFYxCIcYusiKVi1F4yalXGpEqZEgVMsQSMU2mBvL3HmCf4592/TgcDux28PMWo04MQuEVg4+fH0pvH2wOMfWNZioqa2gyViESi9GkDu/RcUilMk45ZRZr1y1ny9a1TDv1QpdfiFvF+7pWwb7tt/Onrt6IzXa0Z7pcrsTHxx8/XxXBQREoFE6hXi5TIlcokcsULb+VKBRKpFJ5t+O22azkZR/i0IG9VJQX0WSuxWJtptFqJEe7CwCxSIpUokQsqUOlCkWtCsHfX41YfDhDxeFwYLVasFjMNFvMWJoP/25bZjHT3Hz0a4vFjN1ux98/ELUqhND4aFLHjsXXV9UmCtttNupMxjbh3/lzeDCgrLCAhrpa7DY7mrQJ+AWojjpGg7GanJw95GsPYLGY8fcPZNzYM0iIH45c3v3iahKJhMDAMAIDw0hOctZysFia0ekqqNGVUV1dhtViobQ6j9JfnaK/t7cffi2DBH5+gc4BA3813t7+vRK9jxTvjxTuW/9uqKs9OmaplAB1EAFBwQSFRTiLNR8h3Cu9vSkoKmLM2PF4+fig9PJG3EHmkV5fSV7+fgoKMrE4mglMDiV2bAoKmRJbs43G2noMVdVUl5SSc2BP22CMTC4nJCLKKfpHxRAaFUNoVHS7mRc2q5WmxgbMjY00NTbQ1NCAuamxZVnDMesaMTc5l8mllraMPoETD0uzGWkPa7J0xqcfLgfg6n9d1eN9VYFqLBYb9SYjdputw89QfxAdG837y95h2Uef8cbLb3PprCt47OlHOGPGaQPSvzvQVzvFfXWQZzyM9pXgsBBEIhFVZeXuDkVAwCMx6pyZ+6FR7cWR2to6nn7sWdau/oVJp5zEMy8uIjgkuN9iCQhUtQk+rbQT+X//hYwNXYv8DoeDhrp6vH19TljB53hos3JxOBw9LqZrs9koryigoCCT4pIcrFYLekMu0dFJxEQnExDQu+8OmUxKaloKqWkpXHjpBYCzHkR+rrZN7D+w/yDfrVjJ50u/BEDppSQ1LYW0EcOQKhXkHsxCV6OnqanJWXutsZHGxiNeNzS2rGuisbGRpsbGttfHrhOJRXh5eeHl5YW3j/O3l7cXJpORHf/swsvb+fdR64/a1hsvby/kcqfVpvB/eGJRWVbBqk++ZNUnXzJi3GhOOesMJk6bjJcbLWcsTQ0oZGJCI9vXVKqpruGJh59mwx8bmTz1ZBY993iPZgH1hIBAFQCGY+yOw2PimPfv+8jL3M9v369g5dIl/PPrz0yfM5eEtBGIRCKazc0c3LWX3Vu2s+ufbVSWOu/tQiPDGTdlEhOmTiZ5RNoJXWNJpVZxxozT2p5V7rz0WtLGjOT2BfcPaBwikchp0SOVQoscFKAK4KKYKC6aOweLxcqenXvYtGEzm/78m9defJPXXnyTkLAQppw6mVOmTebkKZPwP8J6OybeOTBVlF8w5MR9h8OBw+FwuzVzb/EYtei1114jJeXoG5uFCxcyb9485syZw8qVK3n88cdZunTpcdf1F3a7nUP7D/D7qp/Zv3s3VnMjfj4KxiapmDRMjcPhFPqsdTWImuWoQ0JRBYUgk3thabZTW1NHWVkVpcWlWJoshISHEBgahCowAF9/X7y8lcjkUsRip5jTUFdLfW2t87ephuqyw95WXoCXr4TkEaOQyXsu+qhVIYwZfSo7dv5BTs5ukpPHHPfYLUcK1y0WOU4R+/Dr+obaNiG/M/He3z+Q8AgNvj7++Pj44+MTgI+3P3J5x/YqrkAikZI8bATJw0YALaPMhcXs37mT3OxMaqrLkHlL8Av0pclsQixxfqBFIjF+firsNlubWO9wOLrsSyyWIJcrkMkUyGXO3z7efiASYTRWU1ambftfEYslKGQ+yMRKRA4p9mZobrDRWN/Y8uO0lGmoa6CxoZk6o5lao5H5/zcTcA5aFBZlkZOzh6rqEsRiCTExySQnjiYkpOt6ED1BJpMTFhZDWJjzRkSfVskHLz5FaGwUY06dRl29kVqTDm1BJhaL+aj3wtc3AF8fFV4Kb+QyL6RiGSKHGKu5GXNjI40N9ZgbG5wDG12J94FBqAKDSR09DlVgMAFBQS2/g/H18z+uNZWxyUJQWHvP7qamBrQFmeTn70NvqEIsFhMVmYi3tx91dQZMtTrq6oyHZ5EoISA5kMjRGmQyJVgdmOsbMen0ZB/czc7NG5xTa4CAwCCkMnmbcG+1WI7qWyyVIJZKkLT+lkmQe3shVyqQyeVIAqT4BakQSyQ4xPZjQxc4QXA4rMiVfbfkMegNfPvV95xz/jlE9KJAc4BahaXZRkOdiVcevpvEEaNIGTmahLT0Di3EXIlEIuHaG69myrTJPHr/Qu657X7mXHI+/3n03m5PIx1M6KtrADwm06yvSGUy1MFBVJdXujsUAQEA/vr5Vzas/436Sj3Dx45yu12UrqoKhVJKTELyUcv37dnPQ3c/SllpOXfdfwfX3XRNvz90BqjVNNY30Gw2t7M+PFLk3/Dzj0eJ/ONPnU5VWRXZBzLJPXCInAOZ6Kt1BIYEM3LiWNLHjyF9/Bj8VEK2fyt5mc6ZmQmpycfZ0vksVlVVjLYgk6LibJqbm5DLlcTFDsNorMNBE3v2bmLP3k34+6mJjk4mOjqJoMDwPj0PSKVSklOTSE5N4oKLZwPOwYX8PC2Z+w9xYN9BDu7PZOU3P+ItFhER4MdZp8zEZu/6eakVpZeyTbT3anmt9FISHBKMw+GgsaEBXY2OkqJGGhoaaGxsor6uHput89ndnSGRSJw/UgkSsRiJVHr03xIJEqnTv1osEbetk0qcftVyubwtTu+WgQMvL2XbAMORgw1e3l54H7m8ZTu5vPvJaQJ9IzI2mmse+DfGsio2/fI77z33Ch+/8hbjpkxiyllnMHLi2D4Xpu0JdrsdsCJXqtvpN3/9toGFDz9FfV09Dyy4jyuuvqxfr/X+LbN3TccM5LaSMGwE8Q+ksX/H1v9v786j47jLRO9/q6pXqdXd2hfLtmx5ieIlke3scSCekA1PwoHJhOElDNxLGLhcYF5IwJzDkkmYuXguDLwMEO5lcsKdIUPmBpgsxglZyOokjpckjuPEsWXZkmVZsval16r6vX9UdavlLbItyVb38zmn1dWLuutXVf1U1VO/hece+z2/+fk/ESmrIpE0ePfNd0kmnP3DkhXL+fCtH0U3dLa9tJlnHt7IEw89Qigcpvnyi1l5xSUsu6gZf2DiFQ3zzWD/AP09veP6258opRTDw/10HzlIb+9hBgdHOHy4gvLyWryTUPHL6/Ww8uIVrLx4BV/+2hc50t3DphdeZtMLr/D0H//Ew799FMMwWN68jCuuupwrr7qc+Qvn4fF6ONi6/33nfWRklP6+fmfsmb4B+vuccWj6+/vpc6dHR0YpDhUTDpcQiUYIR8KEwyX0D/QzOhijJBImEgkTjpQQiUROqYWbs/xGsvOQ/f4TTg+QTCYJhYopcecjnHNfEi6hJFxCOFJCOBwed18Sdl6f7K4ST8U5k9w/Wm9vL7t27eL+++8HYO3atdxzzz309fWhlDrha2VlU9eE/Qff/hapQefKZGNdCE0LU1pRSWVtHWVV1ZRWVlNWWU1ZVTUlkeik77htyyI2OpJN+sdHh6mf//4HgyeyeNEKOjv3s/2N50kkY5hmOidx7yTtR4aH2Lf/lXEJ2+PRdR2fN0CwKEQkXE5d7bxs4j6TxPd6py55f6o0TaNu7mzq5s7mQ9yUTfY/8egfGNg7yP59u9G9NiXlISIVEVBgpi2stIWVtjFTJumUhWWaWCmLdMp5zkxbKMsmezjrXv0D50A4EYsDilBpMSUVIcLlIUrKnXtfwA1UAUimEyQTSUyPhR6AEl+I8qoKgsXF1NTXUV5bxvbXn2Nf69ukUglCoSjNF17FvHlLCPinvjZCaUUV1370r3j0X/+FcHEZkbIK7JiNEfOQTjitFkw7ha3ZDHsH8PoP4Q36xiXgLdMiHU+SjqdQaYWBQXFxmPPqVxIpKz/l5P2psCyLQ537aG19m45DrShlU1ZWzcoVa2iYex5+//hBRpVSxGLDjIwMMDwy4N4PMjLcz3B8ANNOY0QNqqL1VKHh8/rRlU46kQal0D06mqGBBgqFpayTdjkF4PH48Pn8+H0B/P4g0Yqpq6Unzl22baNpimDRmSewH/z1QyTiCT59+22n9f/F4RL6euIsWXURldUR9r79Fju3vIKuG8xZuIiFSy9g4dILKaucuiTZgkUL+PVvf8X//tm/cN8vfsVrr2zh47f9JbatSCWTJN0+hpOJpNO/cDJFMpUimUiQTKbcwQ4HMAzDfU/K+b9kCq/PS1lZKaVlpZSWRd378dNl5aXZ6WBw6k5U+nt60TQtW7MqH1TUVHHk8NQOxizERKWSSfa/u5d3tr4JODUOz29ezvkrLuD8C5cTneauv3oOH0LTNOYscI7rbdvm1/f/Oz/5wU+prKrkvn//X1y44oJpmZeIm/QZ7Bugsrb6uO8prazi0j/7MEUllbz12ku8+syTvPL0Hxnoi9N7ZJTy6mqaLlzGrIa57H9vL1teeJnnNz6FpmnMXTifZauaWXbRChYuaTqtrmjyRcu771FZW33CCx5KKXr7DnPgwLu0te0mnhjF4/FSP2sBc+eeR031XAzDyHbVEIsNc7CjhYMH9/DOu1vY9c5rBIMhp0b/rAVUVdWPa5F8ugzDYMHCRhYsbGTtR24EnGPrZzc+xa9++FP++tN/xZwFjQSLxhL3gWCQYNCfk+wO4g/4TyuBuW3bNpYvv8Cp7T8aIx6PE4vFs60DstPuLZ1OY1oWlmVhu/emmXlsY1omlvvYsmzn3jSxbTv7PtM0icfj9Pf1E3O/M9Mi4f0qfuUKBPz87L6fsPLiFadcbnHqyqoq+NAN1/Gxz/w/7Hn7XTY99Sybn32RV//0AiWRMJeuuYorPnQ1jU2LpuSiSzw+4rZ+76K7+yCLrliMpjzsa32bivJaPJ4gP/r+/8dDv/kdi85byC9/fS8LFp56EvhUeTweQuGSY1ppZVimRcs7u3n79d0cOjjK8MAwpmnj8egsuXAel15zA6tWXznuAvDVa68nEYuzY8t2tr30KtteeoUXn3gan9/P0lUXsvLKS2m+7GLCBXaBN9PNXcME1qtSiqGhPrq72znc1cbhrrZsHk5ZCs3Q+NNzTmfdkXA5lZWzKC+vpaKilnBJ2Rlvw5VVFXzkL27iI39xE6Zp8tYbO9n0witseuFlfvpPP+en//RzyivKmBsNs/XlLXjCEfr7+uk7OlnuDiCfPqpiY0Yg4M+eTxWHiunv7efAvgMMDg4xPDR83P/JCBYFj7kQEIlG8Pl9DPYP0t+fMy/9A5hp87ifEwgGnHkojRAuDlIRDqHV12CbFp6AHzwe0pbNSCLJ/tYDDA0NMzw4RCJx8rxoZv7WXHs13/j29LbUOGeS+3fccQdKKVauXMlXv/pVOjs7qa6uzg7aYxgGVVVVdHZ2opQ64WtTmdz/s7V/zvNPPcW1a2+kvqGBSLRsUhOO70c3DELhyKT1b6lpGpdecj1PPvXvvLXzFbfGeSDbd30wGMIyNWpq6sb1Z+/z+Z2ucnKeMwzPjK6FkEn2X3D5RaxcudIZBb3tIO+88Ratu/dgWzZ+QwMDtCA5ZdXITGqaRuaBRs57NA3NvQWLiygqLnKaiYaKCRY5jwNFQQyPRtKMEUsMMTjYw8BgD8PD/dkDRo/HSzRaiaaZ/OHxX6FpOrPrF7CgcTnV1XOmffkvu+gy2vbs5o1XXkTTNALBIgJuFziBomLCoVICwbHH/mAQ3aOjdBvTTpMyE8QTo4zGhojFxoK4GUqjRXT80SDFZRGKJymxr5Sir78r2+1OMhknECjmvMUrmNewhGj0xMlzTdPci1VhqqvnHPO5yWSc4ZF+RoYHnfuRQecigDEAkPPbCeDPmc4+9o/9tvy+ID6ff1JOvsTM13/kCIahEzqqG65TFY/FefDf/oOrrr7ytE8adF2nJBpFKS83f+p2bNumo7WFPTvf4L233uSp3z3IU797kIqaWhYuvZCFyy6gft6CSa955PV5+eL/+wVWf/BKvv2Nu/jR+p+Mveb14vf78Pn97r3T5Z/f78fn91ESDmF4Daqrq/D5fAQCAXx+Hz6fl2Qyla210XW4m3d27aa/r//9Dwpzk/9lUYqLiwkEA/gDfgJ+594f8BMIOPMxNh3IeZ8fr8+bjeMDvX2Eo5FprVE21apqq3nnzZ1nezaEAOCam28kOquK6rIKdr2+g13bd/Dac5t47g9PAjCrYY6T7G9eTtOFywjlNEk/U5ZlumNMZbqrHIZQgvL5tcStEd59503u+8W/8eyTL3HV1au56398m3AkPGnf/34yAy0O9o8l9xOxOPt272HP2++y162ZPzQwCIA/EGDe4nmEwx50o5uq2girVq/h0muuz56z2JbFvt172bn1dd7a+job/+M/eezff4s/4GfxsiYWLlnM7Ma5FJcUk4iNEhsZdlqvjo4QHx0hlUwSLi2jtKKS0soqyiqrKa2sIlg0eQNMng373nmPxvMXH/P8wEAPB9re5cCBdxkZHUTXDepq5zF3ziLCoQpGBgfpP9TN/p3vMNjXS9fhw4x2dzjLp6KKS1Zdh+H1cKizlYMH97Jv30727HkDny/ArLr51NcvoLamYVLHzDIMg0s/cAW/+uFPmTd3Nn9+y02T9tnH4/V68HpLxnUXMRks06K78zAd+9s41NZOx/52Og600dl5hFAwwLz6GipqqqmornTvq4iURSkqKcGybbd7oYTb3VD8mAsPtmXTMP/4/a2LqaNpGouWNrFoaRO3/ffb2bFlO5ueeo7n/vAkT/3nBqrqarniQx/kig99kJr6Waf1HfHEKH19Xe7tMH393cTjI9nXA75ilK0IRHy8uvkJAJJJE+U5whe++glu+PCfU1NTPynlnYhIWSmDOd3yDA8OseO1bbzx6lbe2rKdkaFhDMNg0bLzueJDf8b5K5axf/cONv/pKZ577EGGB7q48rq1FJeM7Z8CRUEu/sAVXPyBKzBNk9073nYS/ZteZfumzWi6zqKlTay88lJWXnEpFdWV2a5qE/FRDI+XcLSMYHH+dOeWGYdk7oJju7BRSjHg9uxwsG0v/YPdWLZz3mEm08QHR0kMxtBMjbKKaroOd6A0E39JEfHICAMDPext2QGA1+OjoqKOiopaKsrrKC+vOaWumY/m8XhoXnUhzasu5L9/9Qv0HOnh5RdfZdMLL/Pelu2MDI/w9Hf+B+AMwJ45H6qpraZpyXknrCxVVlZKsCh4wu/NjAuwadPLzJk9h6HBIYYGhxkcHGRoYIihoSEGB4YYGhpmaGCIA/vbGBoaJhFPEI1GKC0rpbauhvOXNh3z/UXBIKNDQ/R1HaGjdT+tu1s4fLCD0f5+RoFoeRnhaIRDbe3Z8z+vz8fSxgbmLJhPw4L51M2dTaSijEQixdDQEMNDIwy5FyWGBt35Ghw6K3H+nDhrfOCBB6itrSWVSvH3f//33H333Xz605+e8u/dufMUTzJ9Pj7w4Q+TVNDSegA48L7/MhPMqrvQ7Vvq2GRiiXvMnE45t1ESQAIYnNZ5nE7bto0Nu1paX01p/fFrLU0GGxhJJxgZzB381UcwUEcwUEd1pUUyNUoyOUIyOcroyAiWbVJRPo9IuAaPx09HRw8dHT2n9f25ZT0dtectp7LxPAyPd0I74EzHMoYBQS8Eg1BWCrZtkUyOEE8MEY8P0t6+h/37dwFO1z6BQJhgIEwwECEQCGMYEw9dppliaLiLwaHDvLf3eTRNI1RcQUX5AoqLSrEtnZaWyfw9Owl7f1k1JxsL0/lNKUaJA/FJ+u7xTjnGiXNOZ3s7AJHS979wrZRicGCQg+0ddBw8REd7Bx3thzjY3kHb/jYG+gf5zN98+ozmxzkRGACcZP/sxoXMblzImptvob+nmz1vvcmenW+y+dkneeXpxwkWh1iwZBkLl05+9z3Lm5fx+8f/L8NDw/gDAXw+b/ai/8mcymBURzcpzW262dc79lxfTx8t77Vkm3OeDk3Tson/yqAfj67zVx+5za3l6HZVkNNtwVjT/0C2n+HcWpKZrg36+/rpOtyd7S4MnBZEY2UcX97jTWfmz9B1NF3HMHQ0zb3XdQxdRzcMdF1D1zP34/s3rqippu/p5zFNM68uWoiZS9M0Zs9vYPb8Bq772E3YlsX+PfvY9fqbvL19By88/hRP/ecGt0b9fJY0L+f8FctZvHzJSftsdrqpHBtnKpYzPRobIpGIHTMfvmIP/nCEne84Y3E1X1ZL82W3EAwWs3nrRkpCUUKhqNPVYShKSSh6TCvDyZKpuf/Sk3/ihcefYu+u3bS3HsiOM1Q7u54LLlnFgiXnseD8xdQ3zM32rdzX3cWLTzzG5mefZOuLz9J8+WqCxSHioyPE3GR9XX0x0egiRoeGnFbD8UO8vfUQb28dPx+Gx0NRcYhgKITP56d19y52bB7fP3SgqJiyyipKK6oode/LKqtIxmMopc7p5NBg/wA9Xd1c63Z1MzIyQGvrLlr3v8PI6AAAfiOI3wwS6x9hx66Xeal/A/ZR3dEEi4qxgY6974573h8IEq2opKyyioryGjxRP0k7QfvBvbTu34VheKirnUd9/QJm1c0/oyRQRihcQmVNNZue/JN7QSZKJHMrixIudZI658J6SafSHD7YQceBdg4daKfjQDsd+9s4fLBj3EX98qpK6ubOZtGHzyedStNzuIsDe/ex/aVXj6mVGo5GqKipoqKmmkr3vrqmisol51FeXUVgClv8iYnzeL2suPwSVlx+CbGRUV574SU2PfUcj/76P3j01w/SsGgBK6+4mGUXNRMoCmKl01i2hbIzLfIVqVSCkdgQo7FBRuODjMSGSJtjx38BXxFFgRLKw9UU+UsI+EPs2r6dzp37WX3jX7C3vYMXn3+GhsZZLL1wAal0jJdefgRwamRnamNXlNcRDpdNSRc9kdIonW0HeeTX/5c3Xt3C3l27UbZNuDRK82UXc+FlF7FsVTNFobGLqPMWLWTV6jW88PijbH3hT7z56ktcds0NLF11CamEc1ErERvNSdjHqK4NsebGy+nrOULfkSOMDvWw+ZmH2frco+j68WOB1+ejJFpKuLSMcLSMSFl5djpc6jzvD0zNPnCy7d+zj6q6GopLQti2TW9PJ/tadnK4q53R+BBozvF2OpEiMRjDTlpEwhXMnrWIuuZ51M6eS0m0FE3T2LZtG4sXLqBj/z46Wlvo2L+P7q52jICXQDhIbHiEzs79Tk1TIFxSSkXFrOy2FImUn3b8rais4KaPruWmj67lkV//Bw/9y7/x4O//Dw0L5xOYxG6XDMMgEo1QXVPF0uVLTvpe00wTj48QT4ySTqfcSpNBAv4gqUSatpZWWnfvpfWtt3n+vb0cPngo+7+lFeXMW7yAy6/5AA2LFtCwqJHS8jL3c0062w6yf08LB/bu48CefWx+9kWefcy5KKfpOrWzZzF3wXzmLpzP3AXzafjA5We920FNnUo7smmwe/duvvCFL/DQQw9x3XXXsXnzZgzDwLIsLrnkEp588kmUUid8bSI195PJJDt37mTp0qXZQXwn6lRHqJ7ppLz561wuq1KK0dEhjvQcoqeng56eQwwM9mSTTZFIBZUVde7V6TpKQuO7wbIsk45DTrc7hzpbUUoRCJSwbMnFzJmzeMpOiM8l27ZtY+nSpacd62a6M4nzMH2/D9u2sSwTyzIxzfS4adMyeXPzK7z7xmaami/jwksvw7Zs+noHONLVy+HObjo7Omlv68wm8kdHR8d9fmlplFmzZzFrdh0XXbKKv/irj55RWf/x699leGCQe/73j0/6vkQ8xr533mbPzjfY+/ZbxEdHst33LHJr9ZdWjO++x7Ztp7m8abpN481s83nbMsea05smlu02mXeTHLphYBge994Yu9ed/nNzn9+xYwcrV67CMIwpaX1nmma2W6BEIkkykSSRSLjTTpdriaTz/PjpBMlkkng8QcuW7WiGQVFNjdv0P1PrL0HCrf2XSCTRdQ2PoWPoGobhJNvHPdZ1bKWwLBvTvR09PZVHgZqmZS8ERIsCVIeKODgcQ+k6uq7j8RjomX6Pdd0Zf0TX8Xg87vMGhp7T97Ghu+tZB6Ww3RNtpWzn4tbgEKFQyDkBR2HbzvOZE3Jb2aAYe16pbB/Mmq7hyVyYAHRdQ3Nb52lKkd3DKAXu/2PbTh+6SqEbHmccFcMZQ0XXx6Y1wymXphtohg66jsbYwF22rUApzl/WxKf+6ycnvHwzv90zjXcz2emW/YXHH+O1F55xWk66/WwbmTFwMutL00ilkiTiCRKJBMl4AqU520agqIiiUDFFoWI8PgNbUyjNBl3BUefNSimwQJkK27SxTRuVtrBSJmbKwkql6evuJpmCZ157j6alC/jsf/sUJZEgIyODjLjdAcYT4+O71+t3k/5jCf9QKILH4wMNNHdGMsdHu955hyXnn4/7ovN6zvvQnG1+ZHCQ7/63O7Btm0AgyNyF85m3aCHzFi9g/uJFhMIlbotUPdsy9ehkQSbJv3PLKyil8AeDBEMhikIhAsVjLTr9gQBevx/TNOnr6aX3SA89Xd2k02l0w6C0spSKmiqi5VF0w4OyFOlkkmQ8QSqeIJmIk4zHSMVipJIJbMtGWTa25fzWNc2DpnsBHdvWsW2yv8VMvHH2DZ5sfNHceyN3f+Lx4PE4+5mxbWT88h1bBArN7YbRuY6q0FDYSrnddToxo6erm11vvMGyK5ajBTV0n7M/SgyOMtIzxEjPILZpUxKOOImtsnKiZeVEyiqc7ivdm88fcLqpWbaMgd4j9Pccob+nm/4jmftuBvp6xi4KaFBcGiZaV4E/HAR3NxguLiMarXS2V6WwsVHZWGlnY6hzn0l02mPP2TZK2cRGR0jE4iTjKdIJk3QyTSppkk6kSSdNbNPG6/XjDxQRLCqmpCRCSSRKtLSUSFkpkdJo9qKAPxgApTBNi3TSHfstmWTnjh00zp/ndCPrdsGXTsXdbmWTpFNJzHQK00pjpp1jqlQyTTKeJDYSY3homNHBYafLVXd7CYWjlFVVUF1bR82c2dQ3zKW+YS5FxaHjbuO2bTsXaA53ceRw9zH3vYe7j0n+l0TCVNXVcvvXv0z9vInX6pRj+tOL812d7Tz1zIPO71HhdJ1rK2zLOd4cu7dQlrsNW7Z7r1C2nX0OTcNfHMBfEsRfHMATGOvvPBVLkhyJkxpJkByJkxxNOP9zHLZl09qj8fobO7n2xmv41t3fJBwJk0ol6es7zJGeQ/T2dtLT20kq5VT+83h8lJfXUFFeS0WFUyM7txte23aOmU3L5M03XqepqSl7PpF53jr6Zppsfv4lOg4cAE0jWhqlqq6Gyrqa7AVeGB/HnDvlPg+J0VEOHdjHQK9TydCJHWPHNk68A4/hweP14vF48fh8Tl/xSiM2MspA3yADvQNYponXHyASjaCwQdlgW+5xm4VSFhxdGUXTnc/3+NB0A133OMdimhelnHyAlXbKa5sWtmlh2WZ2HaNsdxxENe6mgbNv1DLflempwanQomm6e4zn7A88Hi+G14vX55bP78Pn8+Hx+fH6fby5eSvVcyopn1VK2k45XfWC0z1xLE2RP0RlZT1zGhZSM6eBsJvIP57jnbPZlkV3ZwcdrS0cbG2ho20fsfgwgXCQQEkRwUixc+wJeAwvZeU1BPxBdz/ncY91NUBDU9r4den+XpRtu78XJ4Z3tnfw2rMvomznIrrPbaXsDwSc1tMBPz631bQ/00ra78fj9kef/Xx3pWb3xbp7bqZDd88RKqrKUdjYysJWFpYyMW3Tie1WOjuO5fEoW5FOpkkn09iWU/ZAIEioJEI0WkqoxOk23Ovx4fMF8Hr8eDxedMNwzkMMZyBiZ//vPN/f00dbS6uT8HdvvV1Hst9ZWlFOw8L5zFkwn4uuunxCXTEdvW7P5Jj+rFedisViWJZFSUkJSik2btxIU1MT5eXlNDU1sWHDBm6++WY2bNhAU1NTNnl/steEEDObpmnuyWqEeQ1NAKTTKXp7O52Ef+8hDrTtzjZD8/uDTqK/vJZ4fIT9B94llUoQDIZoOm8V8xqWsHfv/vcdOFpMn9bWVtatW8fAwADRaJT169fT0NAw7j2WZfG9732PF190un763Oc+xy233DKl89XVeZBHfn8fmq7x+pY/5CQ+MwchjP3JPB6bcN8/9ppuuDWYDd1JGhqZaWcchonUxKk+bzZ98YP86dmHxr/ghZoGqGmoYYVVBWoFuu7B6/W6XasF8fsDzsG0x4tueNiy9Wnnf7Vs2ofuI91s2z409rnZ147tgqxyfhizbYhf/vP3nbIqJ4l63Gn3wM1fUYa/PArYDMb72Lr1GbZufSZz+EzOV2S+KWdejl4amWTUyY2tp3ETWdtffmz8m4/uak3Lnc4sKS3nYF8b/55xBWHsgDW7LDLPH2c6s60oCHoVQQ8EL51NsLiYcDTsnrQ543RkTjQzyZTM/Dndwo11D6eR83xmnWS2y8yJmVI5iybzf7lJP33sgF/L3U6VW9bclgDZT3Gmj1pBmuac0JqppLNI7JztJJugz5m2GZ+Uzz6fSSKRk1wkW34IuEnZTPkz78tJwuW830mw4y6jnOUzbjkd/fr4kxLbHTTy6G3SeeyeoJJ2NrNxyyvzzNjSa935KjDx5L44fW+99zp1F8x+3/f5CeAnwInrYilSZhIzkXKSl4kUqXiKtHtLJVKYSXPc9jS2/Yzf3vZ39POh6z/Euu/cSVHxsS0DTDPtdvvXn036D48M0NffTfvBPRPq9/tA29b3fQ/ABz9x6bjHKbrZ3dbN7raXj/v+sSR/TsI/pLP46hXYloVpjU9yJhglYY3CKM4NwAfhWQHCs8a6P7QtRSo1QufhQSf57nWS8nhAK4FAiY8APiB63PlSSmUT/k78zCScxv/Os0kelc4msCwFZma9pRWkIBtPlULTtXFdb6JrOfHIff6o92ga7vs0PFUaF9xwIZqmodIKnxWgNFxF+dyasQR+aRnGBFs6eX0+KmtnUVl7bJcitm0z1N+XTfZnLwC0HWE0PoivJECqPMnQaJ8TZ3PicibBk318VMzOLM/cx5qm4fUa+KMBDE+xc8HTY5wgYZXG5AjdscN0DllYe9wkXNoClHPcpOvuveZM6xqdvTudRJt+bOI9y3BvgDek4yVIiCBVnGyw+hjdo7vpfns3298eeza7z8ze3BjubkIaGsFKjTmVpcxdVgY0YdsqOz6bmXaSTOlUmpHh/G0Bfy7p7uqlbfdB/AGfc8xtGE6lAs/YwMqegA/DcLYv3TjJtuSKDcbp7RxkuHeE4d5hhvtG3W3VlXNodew5gsIybVr7hvn7H/wdN950Q/b7fD4/NTVzqamZm33v8MgAPTnJ/l3vvJaNWX5fwKnoYpnHxP59+zdPaPkEyqCxrCGnpWWKnv52evrbGTu+HVse2WMp54Ez3yUByotrsxcPlfsDycaL47BwWsb4gz6qKyuppnJC8ztZco8vx45DcWNb5k2Z9+LUrM8eTzIW13XndvxzuRQmKUxg0eXzAEgkYnjwEg1WUD97IXMbFxMuPfN+8nXDoKZ+DjX1c1i5+moAYiMjHDqwz0n2799HV2c7ml8jUFJEbHAYzY2naJpTmcXQT20+SmDlTavcZXTUvsFW2Yu/yk4Rt5PETIVKHbXcM/tSTXN+kz4Dw+vB8HqceSuCIyMHs19pmxZm2sRKjd3MlOm0rEmZ2JbtfI7HQPe6lTa8zrTH68Hw2Fh6gpGkyeiRPjhybLGcixmWewHILY+VqShkZ7cVTUFJjcay2gZQDc65mWlhpi3SySH2tbzG3ne3883/+aMzWLOn7qwn93t7e/nSl77knrjaNDY28t3vfheAu+66i3Xr1vHzn/+ccDjM+vXrs/93steEEPnH6/Udc9AzONRLT88henoOcaTnEB0dLRiGh/pZjcybt5Sa6jk5O9z9Z23exbG++93v8olPfIKbb76ZRx55hO985zv867/+67j3PPbYY7S1tfHkk08yMDDARz7yES677DLq66euP8ruw92kkym8fi8qm2zGOYh1k4Ga7mZ/s5Udc5PRmcSq89B2a/5YaZN0pkahPZZssN2aEMqtJWRbY7WEbLeGUTptMaiKqKmroqqqgvLKUqKlEcKREgJBH6ZpYllpp7Z/5maZWGaaRCLm1G5wWwUAbhI+M6nc/p97GJcEhnEH5ZnpQFRjTvjEfZGe6uGpyj0LGnsyJ/l51F020ZqdsZwvH//tR52TjH/tBAewY4me8fM27vncI3+VOws5ZckkcY6+J/fxUYnmnCR0JCdRZeDBUGOHa9naNTnJNN1NqGVqpOq6W6tI1xkdHSXg9zsXBGzL2f5UpkbmWA32sRMxNa6m1thfjlr+YwlKjkmUjdXwyrxXN8gOHD8+4UXO9PETZKfr2ATe2PyM23TcdTZ2zSb3wtMJfutTIB0//vgOYvLdsPbjPPvsH4lGS7FMZ9DMdNrETJuk0ybpVJp02iSVTLu3FAl3UO5kMkUiniQRS5COJcC2nRN8LedEWdPRMyf/aNkEpKY578smCXJ+x3/52f/CLZ/8yxPOszP2UsVxxwiybZtYbIiRkUEn2QOM/Uad+5aWFuY3zs/+PjO/96MvVCuOStgqGztnevz9+08bhlPrbdy94XVqwxnOxefMdO57MuN5JWJxp89izUlgezxGNtHrXId0zrQVCtsySZtpWltbqK2twTRNTDNFOp0iHh8lmYjltNw5Ov7ZbvnHP59NVB11IVvTMq179Ow0xzynZ9fv2Gvu82iEwhGWN19KJHyyZPOZ03WdaHkF0fIK5i0+f9xrSiliI8P0H+kmNjLsBL7cGJ5zYXhseYy9Nq4GphtjVW5tfsupfZsduNZ2alxalollm84FbNskTYo0aWzdwvbZKC2zfY4l0FHOckslUk43ELa7/yOzzzPQNcOtvWugazqgUxIpoaKmmmhZBMu2Saec2v2pVJJ0KoWZTpFOJ93ff8o5ZjLT2eMryx1wVyk7s4d0fzEqu39QbgIwu79wt08joGOg42NsfINEOqdChZgy552/hD89s4BwScRpfZVIMjLs3MfjTmvJRDyR08IyQSqVcte5hW2b6IZGSTBA0Oeh/8gwabfLpkxFFN292KS73RbqmZuW+1xmWiNcXcpv/s//YlZ93UnnXdM0wiWlhEtKmT/P6ZrENNP09h2mt7eTkdEhN5Z6svHSMDwcbD/I/MYF45/3eI77Xl0/0QW3yZHdf7jHm3a2goaVvc8cj9o5rx+97xp/n9lbObFm7573WLBgIQrnQlpsZJjYyDCWZTktrAzNvQioZ/e7tlLYtjNgtmW7rYNtpwWHE4/clsK25azHzDJzW2QaupFdfmP3TryxLYVpOhfxzFSadCqFbVosarqAmvrpGyOxKBRiwZLlLFiy3Fletk1PVycHW1voOtiOpoHH68Pj9Tr7Va/XbcE41jrNuaiqOa1Nc47X0Zy4b9nO8srcMhWRMtOpZJJkMkEqmXTiatqNqbbT8joTTwHSiTTJkTRWKoltgrIgEU9SFChxlq/uxef14fWFCPq8eIu8eCOe7Hx7vR78AT/Vs2oJFhWdcL/lXHBQmHbaiftmyjlGMFOkzTSmmcrun7LbQvaWqVzlHiOQ2V457vmmV/Md++QUO+vJ/dmzZ/Pwww8f97XGxkYeeuihU35NCJH/NE0jGqkgGqlgQaOz40om4+i64TT3E+es3t5edu3axf333w/A2rVrueeee+jr6xvXAmvjxo3ccsst6LpOWVkZ11xzDU888QSf/exnp2zeljWvoKq2nq1btnDBBRe4B8SG2xw/U9PHbabn1sCf6c7lLrqmwrlc3nFJJo7tt/50nMvlnYjxJ4VjLReySbKc2sJvvPEGK1aszHl+ck+ijkn2HZ3syiS6ON7FobFk2dhz4y8W+f2TNyaFOLn5jYvoHxie0b+NXLquu33yR0/4nu7uIebMXjR9MzVJAkVBzrtg6Sn9z0B/kiXn58e6nWqaplFcEh43KOa5bibt13K7X8zUsg6FImd7tgqC1+flyg9efkbbSjptkkgkSKfS2XMBj9eT7T5wOseP8Hi8VFfNprrqxK3OhocsGuaeN23zdDLOcZhx3HEdJ0vnoV7q6uZN2efnC03XT9iy61w10+K8bTutFG23a9lAoPj9/3GSnfXkvhBCTJZC6Es/H3R2dlJdXZ0d/NQwDKqqqujs7ByX3O/s7KSubqxmS21tLYcPH57y+auuqaKmrob6OVPXQkCI4xlLSM/8i0aTxbmApjOBsZKztaemylRcMBBCCJG/nJrcPql4NEN5vR683tDZng0hxDks02LH4/G+/5unkCT3hRBC5K2dO3ee9v9u27ZtEufk3FZIZQUpbz4rpLLCuV/ec3V8FSGEENNrIvsDIYQQp0eS+0IIIaZVbW0tXV1dWJaFYRhYlkV3dze1tbXHvO/QoUMsX+50u3R0Tf6JOJ2R5mFmNQU8U4VUVpDy5rNCKiuMlTeZTJ7RhcypdK6OryKEEGJ6TWR/IIQQ4vRIu28hhBDTqry8nKamJjZs2ADAhg0baGpqGtclD8D111/PQw89hG3b9PX18fTTT3PdddedjVkWQghxijLjq6xduxZwxlfZtWsXfX194953ovFVhBBC5IeJ7g+EEEKcHqm5L4QQYtrdddddrFu3jp///OeEw2HWr18PwO23386Xv/xlli1bxs0338ybb77JtddeC8AXv/hFZs8+8SBSQgghzh3n+vgqQgghpsdE9wcnc7ot1M717usmm5Q3vxVSeQuprHDm5ZXkvhBCiGnX2NjIQw89dMzzv/zlL7PThmHwd3/3d9M5W0IIIWYgSfpMTCGVt5DKClLefHeudr02nU6nq81C7a6vUEh581chlRUmp6tNSe4LIYQQQgghJtW5Pr5KoZ44FoJCKitIefPdtm3bWLp06YxO8E90fyCEEOL0FGRyXykFQCqVOq3/TyaTkzk75zwpb/4qpLJC4ZU3E+MyMa+QnGmch8LaXgqprCDlzWeFVFZwynuuxvrc8VVuvvnm9x1f5dprr2VgYICnn36aBx54YELfIcf0p6aQyltIZQUpb747V+P8RE10f3A8EudPjZQ3vxVSeQuprHDmx/Samql7iDMwPDzMe++9d7ZnQwghpsWiRYsoKSk527MxrSTOCyEKzbkY61taWli3bh1DQ0PZ8VXmz58/bnwVy7K4++672bRpE+CMvXLrrbdO6PMl1gshCsm5GOcn6kT7g/cjcV4IUWhOJ9YXZHLftm1GR0fxer1omna2Z0cIIaaEUop0Ok1xcTG6rp/t2ZlWEueFEIVCYr3EeiFEfpM4L3FeCJH/ziTWF2RyXwghhBBCCCGEEEIIIYSYyQrrsq8QQgghhBBCCCGEEEIIkQckuS+EEEIIIYQQQgghhBBCzDCS3BdCCCGEEEIIIYQQQgghZhhJ7gshhBBCCCGEEEIIIYQQM4wk94UQQgghhBBCCCGEEEKIGUaS+0IIIYQQQgghhBBCCCHEDCPJfSGEEEIIIYQQQgghhBBihvGc7RmYKVpbW1m3bh0DAwNEo1HWr19PQ0PD2Z6tSbVmzRp8Ph9+vx+AO+64g9WrV+dF2devX88f//hHOjo6eOyxx1i0aBFw8vU6k8t9ovKeaB3DzC1vf38/X//612lra8Pn8zF37lzuvvtuysrK8nL9nqy8+bh+p1u+L6d8jvNQWLG+kOI8FFaslzg/tQphOeVzrC+kOA+FFesLKc6DxPqplu/LKZ/jPBRWrC+kOA+FFeunNc4rMSG33Xabevjhh5VSSj388MPqtttuO8tzNPmuvvpqtXv37mOez4eyb9myRR06dOiYMp6sbDO53Ccq74nWsVIzt7z9/f3q1VdfzT7+/ve/r775zW8qpfJz/Z6svPm4fqdbvi+nfI7zShVWrC+kOK9UYcV6ifNTqxCWUz7H+kKK80oVVqwvpDivlMT6qZbvyymf47xShRXrCynOK1VYsX4647wk9yegp6dHrVy5UpmmqZRSyjRNtXLlStXb23uW52xyHW/jyrey55bxZGXLl3JPdAeRL+VVSqknnnhC/fVf/3VBrF+lxsqrVGGs36lUCMupEOK8UoUV6wsxzitVWLFe4vzkKZTlVAixvpDivFKFGesLKc4rJbF+MhXCciqEOK9UYcX6QozzShVWrJ/KOC/d8kxAZ2cn1dXVGIYBgGEYVFVV0dnZSVlZ2Vmeu8l1xx13oJRi5cqVfPWrX83rsp+sbEqpvC330es4HA7nzXq2bZvf/OY3rFmzpiDWb255M/J5/U61QllOhRTnoTBjfb7HgUKK9RLnJ1chLadCivX5HgdOJJ9jQSHFeZBYP9kKZTkVUpyHwoz1+R4HCinWT3WclwF1RdYDDzzAo48+yu9+9zuUUtx9991ne5bEJMv3dXzPPfdQVFTEJz/5ybM9K9Pi6PLm+/oVZ062kfxXCOu4kGK9xHlxOmQ7yX/5vo4LKc6DxHpx6mQbyX+FsI4LKdZPdZyX5P4E1NbW0tXVhWVZAFiWRXd3N7W1tWd5ziZXpjw+n49PfOITbN++Pa/LfrKy5Wu5j7eOM8/P9PKuX7+eAwcO8OMf/xhd1/N+/R5dXsjv9TsdCmE5FVqch8KL9fkeBwop1kucn3yFspwKLdbncxw4kXyOBYUU50Fi/VQohOVUaHEeCi/W53scKKRYPx1xXpL7E1BeXk5TUxMbNmwAYMOGDTQ1Nc2Y5h8TEYvFGB4eBkApxcaNG2lqasrrsp+sbPlY7hOtY5j52/iPfvQjdu7cyc9+9jN8Ph+Q3+v3eOXN5/U7XfJ9ORVinIf8jgVHy/c4UEixXuL81CiE5VSIsT5f48CJ5HMsKKQ4DxLrp0q+L6dCjPOQ37HgaPkeBwop1k9XnNeUUmoKy5E3WlpaWLduHUNDQ4TDYdavX8/8+fPP9mxNmvb2dr70pS9hWRa2bdPY2Mi3vvUtqqqq8qLs3/ve93jyySfp6emhtLSUaDTKH/7wh5OWbSaX+3jl/cUvfnHCdQwzt7x79uxh7dq1NDQ0EAgEAKivr+dnP/tZXq7fE5V33bp1ebl+p1s+L6d8j/NQWLG+kOI8FFaslzg/tfJ9OeV7rC+kOA+FFesLKc6DxPqpls/LKd/jPBRWrC+kOA+FFeunM85Lcl8IIYQQQgghhBBCCCGEmGGkWx4hhBBCCCGEEEIIIYQQYoaR5L4QQgghhBBCCCGEEEIIMcNIcl8IIYQQQgghhBBCCCGEmGEkuS+EEEIIIYQQQgghhBBCzDCS3BdCCCGEEEIIIYQQQgghZhhJ7ouCdejQIZqbm7Es62zPihBCiCkisV4IIfKbxHkhhMh/EuuFODFJ7ouCsmbNGl5++WUA6urqeP311zEMY9q+f/PmzVx11VXT9n1CCFGIJNYLIUR+kzgvhBD5T2K9EBMjyX0hhBBCCCGEEEIIIYQQYoaR5L4oGHfeeSeHDh3i85//PM3Nzfzyl79k8eLFmKYJwG233caPfvQjPv7xj9Pc3MznP/95+vv7+drXvsaKFSv42Mc+xsGDB7Of19LSwmc+8xkuvvhirrvuOjZu3Jh97fnnn+fGG2+kubmZ1atXc9999xGLxbj99tvp7u6mubmZ5uZmurq62LFjB7feeiurVq3iyiuv5O677yaVSmU/a/HixTzwwANce+21NDc38+Mf/5i2tjZuvfVWVqxYwVe+8pXs+zNXln/xi19wySWXsGbNGh599NFpWsJCCHH2SawXQoj8JnFeCCHyn8R6IU6BEqKAXH311WrTpk1KKaXa29vVokWLVDqdVkop9clPflJdc8016sCBA2poaEjdcMMN6tprr1WbNm1S6XRa3XnnnWrdunVKKaVGR0fVVVddpX7729+qdDqtdu7cqS6++GL13nvvKaWUuuKKK9SWLVuUUkoNDAyonTt3KqWUevXVV9Xq1avHzdNbb72lXn/9dZVOp1V7e7u6/vrr1f333599fdGiRepv/uZv1PDwsHrvvffUkiVL1Kc+9SnV1taWnc/f//732c9vampS//AP/6CSyaTavHmzuuCCC1RLS8vULVQhhDjHSKwXQoj8JnFeCCHyn8R6ISZGau4LkeOjH/0oc+bMoaSkhKuuuorZs2dz+eWX4/F4uP7669m1axcAzz33HLNmzeJjH/sYHo+HJUuWcN111/HHP/4RAI/Hw969exkZGSESibBkyZITfufSpUu58MIL8Xg81NfXc+utt7Jly5Zx77n99tsJhUIsXLiQRYsWccUVVzB79uzsfGbmK+MrX/kKPp+Piy++mA984AM8/vjjk7ykhBBi5pJYL4QQ+U3ivBBC5D+J9UI4PGd7BoQ4l1RUVGSn/X7/uMeBQIBYLAZAR0cHO3bsYNWqVdnXLcvipptuAuAnP/kJ9957Lz/84Q9ZvHgxX/va12hubj7ud7a2tvL973+fnTt3Eo/HsSzrmJ3JyebL7/fT09OTfRwOhykqKso+rquro7u7+5SWgxBC5DOJ9UIIkd8kzgshRP6TWC+EQ5L7QpyG2tpaLrroIu6///7jvr58+XLuvfde0uk0DzzwAH/7t3/L888/j6Zpx7z3rrvu4vzzz+eHP/whoVCIX/3qV9kryKdjaGiIWCyW3UF0dnaycOHC0/48IYQoVBLrhRAiv0mcF0KI/CexXuQ76ZZHFJSKigra29vP+HM++MEPsn//fh5++GHS6TTpdJodO3bQ0tJCKpXi0UcfZXh4GK/XS3FxMYZhAFBeXs7AwADDw8PZzxodHaW4uJji4mJaWlr4zW9+c8bz98///M+kUim2bt3Kc889x/XXX3/GnymEEDOFxHohhMhvEueFECL/SawXYmIkuS8Kyuc+9znuvfdeVq1adUZXV0OhEPfddx8bN25k9erVXHnllfzgBz/Ijnr+yCOPsGbNGlasWMGDDz7IP/7jPwLQ2NjIhz/8Ya655hpWrVpFV1cX3/jGN9iwYQMrVqzg29/+NjfeeOMZlbGiooJwOMzq1au54447uOuuu2hsbDyjzxRCiJlEYr0QQuQ3ifNCCJH/JNYLMTGaUkqd7ZkQQkyOzZs3c+edd/LCCy+c7VkRQggxRSTWCyFEfpM4L4QQ+U9ivZgsUnNfCCGEEEIIIYQQQgghhJhhJLkvhBBCCCGEEEIIIYQQQsww0i2PEEIIIYQQQgghhBBCCDHDSM19IYQQQgghhBBCCCGEEGKGkeS+EEIIIYQQQgghhBBCCDHDSHJfCCGEEEIIIYQQQgghhJhhJLkvhBBCCCGEEEIIIYQQQswwktwXQgghhBBCCCGEEEIIIWYYSe4LIYQQQgghhBBCCCGEEDPM/w+Zs73eI6sUggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1872x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "fig,axes = plt.subplots(nrows = 1,ncols=4,figsize=(26,4))\n",
    "\n",
    "# palette=sns.cubehelix_palette(4,\n",
    "#     start=0.00,\n",
    "#     rot=1.00,\n",
    "#     gamma=0.60,\n",
    "#     hue=0.80,\n",
    "#     light=0.3,\n",
    "#     dark=0,\n",
    "#     reverse=True,\n",
    "#     as_cmap=False)\n",
    "palette=sns.cubehelix_palette(n_colors=4,start=2.10,rot=1.00,gamma=2.00,hue=0.40,light=0.80,dark=0.40,reverse=True)\n",
    "# perf_total_df = perf_total_df.loc[perf_total_df.platform=='kubelss']\n",
    "\n",
    "\n",
    "node_perf_cpu_migrations_total = perf_total_df.loc[(perf_total_df.metrics=='node_perf_cpu_migrations_total')]\n",
    "node_perf_branch_misses_total = perf_total_df.loc[(perf_total_df.metrics=='node_perf_branch_misses_total')]\n",
    "node_perf_context_switches_total = perf_total_df.loc[(perf_total_df.metrics=='node_perf_context_switches_total')]\n",
    "node_perf_cache_bpu_read_misses_total = perf_total_df.loc[(perf_total_df.metrics=='node_perf_cache_bpu_read_misses_total')]\n",
    "\n",
    "# axes[0].set_yscale('log')\n",
    "# axes[1].set_yscale('log')\n",
    "# axes[2].set_yscale('log')\n",
    "# axes[3].set_yscale('log')\n",
    "\n",
    "# axes[0].set_xlable('log')\n",
    "# axes[1].set_yscale('log')\n",
    "# axes[2].set_yscale('log')\n",
    "# axes[3].set_yscale('log')\n",
    "\n",
    "sns.lineplot(ax=axes[0], x='timestamp',y=\"value\",\n",
    "             palette=palette,hue=\"platform\",\n",
    "             data=node_perf_cpu_migrations_total)\n",
    "\n",
    "sns.lineplot(ax=axes[1], x='timestamp',y=\"value\",\n",
    "             palette=palette,hue=\"platform\",\n",
    "             data=node_perf_context_switches_total)\n",
    "\n",
    "sns.lineplot(ax=axes[2], x='timestamp',y=\"value\",\n",
    "             palette=palette,hue=\"platform\",\n",
    "             data=node_perf_cache_bpu_read_misses_total)\n",
    "\n",
    "sns.lineplot(ax=axes[3], x='timestamp',y=\"value\",\n",
    "             palette=palette,hue=\"platform\",\n",
    "             data=node_perf_branch_misses_total)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "09d1234c-e977-470e-abba-32deef2834eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7cede6e5ece4b51ac14576c4f26429a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=9, description='n_colors', max=16, min=2), FloatSlider(value=0.0, descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[0.9312692223325372, 0.8201921796082118, 0.7971480974663592],\n",
       " [0.8888663743660877, 0.7106793139856472, 0.7158661451411206],\n",
       " [0.8314793143949643, 0.5987041921652179, 0.6530062709235388],\n",
       " [0.7588951019517731, 0.49817117746394224, 0.6058723814510268],\n",
       " [0.6672565752652589, 0.40671838146419587, 0.5620016466433286],\n",
       " [0.5529215689527474, 0.3217924564263954, 0.5093718054521851],\n",
       " [0.43082755198027817, 0.24984535814964698, 0.44393960899639856],\n",
       " [0.29794615023641036, 0.18145907625614888, 0.35317781405034754],\n",
       " [0.1750865648952205, 0.11840023306916837, 0.24215989137836502]]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.choose_cubehelix_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "e67d43a1-57d0-49fc-956a-78aef3aa7c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "handling -----------172 172.169.8.193\n",
      "pod openwhisk-invoker-0  in node 172\n",
      "WARNING: No swap limit support\n",
      "221282204220840\n",
      "pod openwhisk-kafkaprovider-6bffd8bbf5-7lvjv  in node 172\n",
      "WARNING: No swap limit support\n",
      "Problems finding threads of monitor\n",
      "\n",
      " Usage: perf stat [<options>] [<command>]\n",
      "\n",
      "    -p, --pid <pid>       stat events on existing process id\n",
      "    -t, --tid <tid>       stat events on existing thread id\n",
      "20806\n",
      "pod wskopenwhisk-invoker-00-1-prewarm-nodejs10  in node 172\n",
      "WARNING: No swap limit support\n",
      "22042\n",
      "pod wskopenwhisk-invoker-00-2-prewarm-nodejs10  in node 172\n",
      "WARNING: No swap limit support\n",
      "22128\n"
     ]
    }
   ],
   "source": [
    "!bash perf.sh 'LLC-load-misses,branch-misses' 500 openwhisk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "32557a04-7cfd-4ef8-92eb-11fb160bd2bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172.tar.gz                                    100% 4025     5.6MB/s   00:00    \n"
     ]
    }
   ],
   "source": [
    "!bash perf_stop_get_data.sh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "90e29aaa-5d41-48dc-ad77-b272ece03204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tar: Removing leading `/' from member names\n",
      "/home/tmp/\n",
      "/home/tmp/172_openwhisk-invoker-0_1630293797.228112229.csv\n",
      "/home/tmp/172_wskopenwhisk-invoker-00-1-prewarm-nodejs10_1630293797.228112229.csv\n",
      "/home/tmp/172_openwhisk-kafkaprovider-6bffd8bbf5-7lvjv_1630293797.228112229.csv\n"
     ]
    }
   ],
   "source": [
    "!tar -zxvf perf/172.tar.gz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f77c9e54-75bb-4601-83f5-d3165507f937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>count</th>\n",
       "      <th>unit</th>\n",
       "      <th>events</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.500402</td>\n",
       "      <td>247119</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(25.63%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000719</td>\n",
       "      <td>252692</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(59.80%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.500951</td>\n",
       "      <td>167979</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(60.65%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.001174</td>\n",
       "      <td>232115</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(66.66%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.501486</td>\n",
       "      <td>152920</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(69.50%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.001741</td>\n",
       "      <td>263830</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(58.52%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.501961</td>\n",
       "      <td>169590</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(40.77%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.002216</td>\n",
       "      <td>248160</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(68.49%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.502493</td>\n",
       "      <td>241353</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(42.66%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.002749</td>\n",
       "      <td>1334945</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(31.47%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.502962</td>\n",
       "      <td>183530</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(62.92%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.003193</td>\n",
       "      <td>232921</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(42.04%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.503433</td>\n",
       "      <td>180737</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(20.79%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.003692</td>\n",
       "      <td>271053</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(30.76%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.503911</td>\n",
       "      <td>173737</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(50.79%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.004169</td>\n",
       "      <td>251769</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(23.77%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.504411</td>\n",
       "      <td>184314</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(49.32%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9.004658</td>\n",
       "      <td>248993</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(36.44%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.504883</td>\n",
       "      <td>184398</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(41.17%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.005117</td>\n",
       "      <td>309171</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(41.42%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.505379</td>\n",
       "      <td>228099</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(44.00%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11.005689</td>\n",
       "      <td>272323</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(52.73%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11.505910</td>\n",
       "      <td>197695</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(44.95%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.006165</td>\n",
       "      <td>255150</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(56.89%)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12.506341</td>\n",
       "      <td>184969</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(38.77%)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time    count    unit    events\n",
       "0    0.500402   247119  cycles  (25.63%)\n",
       "1    1.000719   252692  cycles  (59.80%)\n",
       "2    1.500951   167979  cycles  (60.65%)\n",
       "3    2.001174   232115  cycles  (66.66%)\n",
       "4    2.501486   152920  cycles  (69.50%)\n",
       "5    3.001741   263830  cycles  (58.52%)\n",
       "6    3.501961   169590  cycles  (40.77%)\n",
       "7    4.002216   248160  cycles  (68.49%)\n",
       "8    4.502493   241353  cycles  (42.66%)\n",
       "9    5.002749  1334945  cycles  (31.47%)\n",
       "10   5.502962   183530  cycles  (62.92%)\n",
       "11   6.003193   232921  cycles  (42.04%)\n",
       "12   6.503433   180737  cycles  (20.79%)\n",
       "13   7.003692   271053  cycles  (30.76%)\n",
       "14   7.503911   173737  cycles  (50.79%)\n",
       "15   8.004169   251769  cycles  (23.77%)\n",
       "16   8.504411   184314  cycles  (49.32%)\n",
       "17   9.004658   248993  cycles  (36.44%)\n",
       "18   9.504883   184398  cycles  (41.17%)\n",
       "19  10.005117   309171  cycles  (41.42%)\n",
       "20  10.505379   228099  cycles  (44.00%)\n",
       "21  11.005689   272323  cycles  (52.73%)\n",
       "22  11.505910   197695  cycles  (44.95%)\n",
       "23  12.006165   255150  cycles  (56.89%)\n",
       "24  12.506341   184969  cycles  (38.77%)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_perf=pd.read_csv('perf/home/tmp/cycles_172_hello-java-66ddd95588-m6svb_1630248661.808489808.csv',\n",
    "                    delim_whitespace=True,header=None,skiprows=[0,1,2])\n",
    "df_perf.columns=perf_columns\n",
    "df_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f963c9b0-bd0c-44fd-b7ce-e7ae61465a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1630248661.8084898"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params='cycles_172_hello-java-66ddd95588-m6svb_1630248661.808489808.csv'.split('_')\n",
    "params\n",
    "perf_start= float(params[2].replace('.csv',''))\n",
    "perf_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "cdcadd3b-a5d1-4ed4-a274-f8a62439bd46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>count</th>\n",
       "      <th>unit</th>\n",
       "      <th>events</th>\n",
       "      <th>node</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>functions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.500402</td>\n",
       "      <td>247119</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(25.63%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000719</td>\n",
       "      <td>252692</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(59.80%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.500951</td>\n",
       "      <td>167979</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(60.65%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.001174</td>\n",
       "      <td>232115</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(66.66%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.501486</td>\n",
       "      <td>152920</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(69.50%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.001741</td>\n",
       "      <td>263830</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(58.52%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.501961</td>\n",
       "      <td>169590</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(40.77%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.002216</td>\n",
       "      <td>248160</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(68.49%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.502493</td>\n",
       "      <td>241353</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(42.66%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.002749</td>\n",
       "      <td>1334945</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(31.47%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.502962</td>\n",
       "      <td>183530</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(62.92%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6.003193</td>\n",
       "      <td>232921</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(42.04%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6.503433</td>\n",
       "      <td>180737</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(20.79%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7.003692</td>\n",
       "      <td>271053</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(30.76%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.503911</td>\n",
       "      <td>173737</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(50.79%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.004169</td>\n",
       "      <td>251769</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(23.77%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.504411</td>\n",
       "      <td>184314</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(49.32%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9.004658</td>\n",
       "      <td>248993</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(36.44%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9.504883</td>\n",
       "      <td>184398</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(41.17%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.005117</td>\n",
       "      <td>309171</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(41.42%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.505379</td>\n",
       "      <td>228099</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(44.00%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11.005689</td>\n",
       "      <td>272323</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(52.73%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11.505910</td>\n",
       "      <td>197695</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(44.95%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.006165</td>\n",
       "      <td>255150</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(56.89%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12.506341</td>\n",
       "      <td>184969</td>\n",
       "      <td>cycles</td>\n",
       "      <td>(38.77%)</td>\n",
       "      <td>172</td>\n",
       "      <td>1.630249e+09</td>\n",
       "      <td>hello-java-66ddd95588-m6svb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time    count    unit    events node     timestamp  \\\n",
       "0    0.500402   247119  cycles  (25.63%)  172  1.630249e+09   \n",
       "1    1.000719   252692  cycles  (59.80%)  172  1.630249e+09   \n",
       "2    1.500951   167979  cycles  (60.65%)  172  1.630249e+09   \n",
       "3    2.001174   232115  cycles  (66.66%)  172  1.630249e+09   \n",
       "4    2.501486   152920  cycles  (69.50%)  172  1.630249e+09   \n",
       "5    3.001741   263830  cycles  (58.52%)  172  1.630249e+09   \n",
       "6    3.501961   169590  cycles  (40.77%)  172  1.630249e+09   \n",
       "7    4.002216   248160  cycles  (68.49%)  172  1.630249e+09   \n",
       "8    4.502493   241353  cycles  (42.66%)  172  1.630249e+09   \n",
       "9    5.002749  1334945  cycles  (31.47%)  172  1.630249e+09   \n",
       "10   5.502962   183530  cycles  (62.92%)  172  1.630249e+09   \n",
       "11   6.003193   232921  cycles  (42.04%)  172  1.630249e+09   \n",
       "12   6.503433   180737  cycles  (20.79%)  172  1.630249e+09   \n",
       "13   7.003692   271053  cycles  (30.76%)  172  1.630249e+09   \n",
       "14   7.503911   173737  cycles  (50.79%)  172  1.630249e+09   \n",
       "15   8.004169   251769  cycles  (23.77%)  172  1.630249e+09   \n",
       "16   8.504411   184314  cycles  (49.32%)  172  1.630249e+09   \n",
       "17   9.004658   248993  cycles  (36.44%)  172  1.630249e+09   \n",
       "18   9.504883   184398  cycles  (41.17%)  172  1.630249e+09   \n",
       "19  10.005117   309171  cycles  (41.42%)  172  1.630249e+09   \n",
       "20  10.505379   228099  cycles  (44.00%)  172  1.630249e+09   \n",
       "21  11.005689   272323  cycles  (52.73%)  172  1.630249e+09   \n",
       "22  11.505910   197695  cycles  (44.95%)  172  1.630249e+09   \n",
       "23  12.006165   255150  cycles  (56.89%)  172  1.630249e+09   \n",
       "24  12.506341   184969  cycles  (38.77%)  172  1.630249e+09   \n",
       "\n",
       "                      functions  \n",
       "0   hello-java-66ddd95588-m6svb  \n",
       "1   hello-java-66ddd95588-m6svb  \n",
       "2   hello-java-66ddd95588-m6svb  \n",
       "3   hello-java-66ddd95588-m6svb  \n",
       "4   hello-java-66ddd95588-m6svb  \n",
       "5   hello-java-66ddd95588-m6svb  \n",
       "6   hello-java-66ddd95588-m6svb  \n",
       "7   hello-java-66ddd95588-m6svb  \n",
       "8   hello-java-66ddd95588-m6svb  \n",
       "9   hello-java-66ddd95588-m6svb  \n",
       "10  hello-java-66ddd95588-m6svb  \n",
       "11  hello-java-66ddd95588-m6svb  \n",
       "12  hello-java-66ddd95588-m6svb  \n",
       "13  hello-java-66ddd95588-m6svb  \n",
       "14  hello-java-66ddd95588-m6svb  \n",
       "15  hello-java-66ddd95588-m6svb  \n",
       "16  hello-java-66ddd95588-m6svb  \n",
       "17  hello-java-66ddd95588-m6svb  \n",
       "18  hello-java-66ddd95588-m6svb  \n",
       "19  hello-java-66ddd95588-m6svb  \n",
       "20  hello-java-66ddd95588-m6svb  \n",
       "21  hello-java-66ddd95588-m6svb  \n",
       "22  hello-java-66ddd95588-m6svb  \n",
       "23  hello-java-66ddd95588-m6svb  \n",
       "24  hello-java-66ddd95588-m6svb  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_perf['node']=params[0]\n",
    "df_perf['timestamp']=df_perf['time']+perf_start\n",
    "df_perf['functions']=params[1]\n",
    "df_perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "503dcc3f-ccd1-4ba8-af19-74fae4fb4229",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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